Escherichia, Pseudomonas, Streptococcus, Staphylococcus and Aeromonas have been sampled without and with periodic LDUVR, respectively. A new hypothesis was put up to clarify the construction and maintenance process of high microbiological diversity in artificial intelligence devices by comparing and evaluating the variations between the dynamic response characteristics of their relative abundances in the two scenarios as follows: the periodic LDUVR can be regarded as an adverse factor with intermediate disturbance, causing stronger microbial stochastic growth responses (SGR) which would inevitably give rise to stronger random variation of the other important processes tightly correlated with SGR, such as intra- and interspecific competition process, and substrates production and consumption process, which could effectively diminish the auto- and cross-correlation of stochastic processes of microbial populations, alleviating the intra- and inter-specific competitions. In artificial intelligence devices with LDUVR, these crucial succession processes can propel the microbial communities to generate and sustain a high species diversity. Finally, thorough Monte Carlo simulations were used to thoroughly confirm the idea. This research can build the theoretical groundwork, offer fresh viewpoints, and suggest potential microbial prevention strategies for the succession of microbial communities in LDUVR.The development of artificial intelligence devices in the complementary medicine field is rapid and the surface microbial diversity pollution was found with periodic low-dose ultraviolet radiation (LDUVR). Since artificial intelligence devices do not have enough different types of substrates for microbial communities, it is unclear how the great microbial diversity can emerge and persist, as this clearly defies the competitive exclusion principle of ecology. In this study, the 5 most common genera in the artificial intelligence devices,  Microbial diversity in the medical treatment environment, such as operating room, sickroom, treatment room, and injection , is often at a lower level, and all sections of the environment need to be properly cleaned, disinfected, and maintained. However, microbial diversity with high richness and evenness has been currently found in the artificial intelligence devices where periodic ultraviolet (UV) ray disinfection was used extensively, because various kinds of microbial species had been sampled and identified from the parts which were uneasily cleaned in the devices, such as screw, pulley, and backboard, where the microbial diversity is significantly higher than other parts of artificial intelligence devices. The microorganisms on the significant surface are killed by direct exposure to periodic high-dose UV radiation, nevertheless, those microorganisms colonized on the positions with a relatively long distance the from UV source can only receive periodic low-dose UV radiation (LDUVR) due to energy degradation , 2.The indoor microorganisms are often combined with dust particles and exist in the form of aerosol suspending in the air and precipitating in artificial intelligence devices with periodic LDUVR . The dusA negative disturbance for the microbial community succession in artificial intelligence devices could be the periodic LDUVR. The intermediate disturbance hypothesis has been put forth to suggest that species diversity will be higher in communities with moderate levels of perturbations than in communities with no perturbations or communities with rare or very frequent perturbations . As is wThe periodic LDUVR can be viewed as an unfavorable factor with intermediate disturbance, causing microbial stochastic growth response (SGR) along with random variation of the crucial processes tightly correlated with SGR, such as intra- and inter-specific competition , 9. ThisEscherichia, Pseudomonas, Streptococcus, Staphylococcus, and Aeromonas in artificial intelligence devices were periodically sampled and analyzed. To describe the dynamic mechanisms causing microbial community successions in artificial intelligence devices with periodic LDUVR, highly valid kinetic models expressed by differential equations with kinetic parameters obeying different normal distributions were established based on the proposed assumptions, experimental phenomena, and data ..Monte CaIt is important to note that the digital simulation results of the microbial community succession patterns that emerged in the artificial intelligence device with periodic LDUVR, as shown in Due to the PSD's modest setting, however, values chosen at random from the value range of the simulation results suggested that, subjected to competitive exclusion, the majority of microbial species would eventually go extinct, with only a few species whose number is no more than the substrate types produced by microbial decomposition being able to coexist , due to Monte Carlo simulations were performed to sufficiently validate and confirm the proposed theory. The findings of this study could create the theoretical groundwork for understanding the ecological impact of LDUV on the succession of microbial communities and offer practical advice on microbial prevention and control in the context of LDUVR.We theoretically provide a hypothesis on the production and preservation of microbial diversity in the artificial intelligence devices with intermediate disturbance brought on by periodic LDUVR based on microbial ecology, experimental phenomena, and data. The periodic LDUVR could produce intermediate disturbance that would lead to stronger microbial SGR along with stronger random process variations of substrate production and consumption. This would significantly reduce the auto- and cross-correlation of microbial populations stochastic processes and effectively alleviate intra- and inter-specific competition to form and maintain a microbial community with higher richness. The hypothesized hypothesis was then quantified using a collection of kinetic models written by differential equations with parameters obeying various normal distributions. Finally, a significant number of


The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate. The recent advancement of Internet of Things (IoT) represents a paradigm shift of modern global communication infrastructure . It revoFlooding-based DDoS attacks cause consistent threat towards the smart industrial service providers of IoT. Frequently, such attacks follow the networking protocols and cause extensive network traffic and drain the network resources. Such intense penetrations of DDoS attacks result in the expensive exhaustion of servers and individual hosts’ communication capacity to respond to legitimate users. Usually, DDoS attackers explore UDP Flood, PING Flood, SYN Flood, and HTTP Flood to launch DDoS attacks ,8,9. AccAttackers on network security have advanced to a high level of technical competence. For normally built dispersed networks, they can modify packet header data and execute legitimate extensive service requests on specific workstations or servers ,15. HoweRecently, machine learning classifier algorithms are being integrated to upgrade the Intrusion Detection Systems and Intrusion Prevention Systems. Machine learning classification and regression algorithms enhance the intelligence of the system to differentiate the anonymous traffic from normal network traffic ,26,27. MWe develop a novel AMLSDM framework that supports feed-forwarding through adaptive machine learning classification for DDoS detection and mitigation system for SDN-enabled IoT. We design the module of the adaptive machine learning model with the support of the EV classification model, which accumulates the measurements of SVM, GNB, RF, KNN, and LR machine learning classifiers.We develop the inspection module for feature extraction of real-time network traffic and compute the entry for current network flow. Then, we integrate the trained module of the adaptive machine learning model with an environment-specific dataset for real-time network traffic flow to detect the DDoS attacks.We configure two custom topologies of SDN enabled IoT networks for experimentation in a virtual environment using Mininet, OF switches using Open vSwitch, remote POX, and Floodlight SDN controllers. The sFlow network flow analyzer is also used to record real-time statistics. On the LINUX machines that have been switched to OF switches, the Host sFlow Daemon is installed. The Sflow-RT run on the monitoring server that collects the real-time statistics of the OF switches.We launch the different DDoS attacks to test the detection and mitigation capability of AMLSDM framework and achieves promising simulation results.To this end, we propose the AMLSDM framework to implement adaptive machine learning classification to detect and mitigate the DDoS attacks for SDN-enabled IoT networks. The proposed AMLSDM framework contains the following building blocks: training of multilayered feed-forwarding approach based adaptive machine learning model over the environment-specific dataset, feature extraction of real-time SDN-enabled IoT network, inspection and detection of DDoS attacks, and DDoS mitigation system. Our proposed model is the extension of our conference paper . In the The rest of the paper is organized as follows: SDN provides networking applications for all the wired and wireless networks, including IoT. Recently, many research papers have been published, and many industrial solutions are also available, in which SDN is being implemented for wireless networks, including IoT networks, to enhance the security features of the IoT networks. The extensive deployment of IoT-equipped devices has reached billions after the emergence of intelligent civilian healthcare and military surveillance applications. Due to limited computational and communication resources, these IoT-equipped networks have experienced millions of attacks due to a lack of security concerns in the design of the devices ,8. HowevRecent literature has proposed certain network-based strategies based on SDN solutions, with the lead foundational framework to go forward in fixing the issue of DDoS attacks. In , a machiIn CONA, the requests pattern is monitored, and servers use content-based thresholds to reduce DDoS effects. Some review work ,37 indicTo this end, in this paper, we propose the AMLSDM framework, which is an adaptive machine learning-based DDoS detection and mitigation system, to deal with the limitations of existing solutions. Our solutions utilize the wireless applications of SDN with the help of available setting for wireless networking in Mininet. Most importantly, at forwarding layers, we have associated IoT devices in our simulation’s configurations. Furthermore, we have selected the most popular and successful machine algorithms, i.e., SVM, GNB, RF, KNN, and LR, for DDoS detection during the implementation of the EV model. These machine algorithms are widely accepted and cited by the recent research work for DDoS detection in IoT networks.In this section, we first present the network topology and then discuss machine learning classifiers. Lastly, we briefly define the threat model, which is a major motivation to the proposed framework.The SDN-enabled IoT network topology is launched by Mininet emulator, which is being coordinated to remote SDN controllers of POX and floodlight at the control layer. The deployed IoT hosts are connected with OF switches at the forwarding plane. The primary responsibility of these OF switches is to notify the SDN controller of any new incoming communication requests. In practice, OF switches send DDoS attack reports to the SDN controller, as well as the congestion rate over available communication lines. The DDoS built-in congestion control system is reactively performed by the SDN controller. Initially, OF switches execute the trained adaptive machine learning classification designed by combining SVM, GNB, RF, KNN, and LR at the EV model. For genuine host requests in mitigation response, the SDN controller additionally controls alternate path computation based on link flow congestion statistics and operational ports. OF switches function as intermediary devices, performing path returns and reporting statistics in bytes on a regular basis in accordance with the flow rules. Meanwhile, the SDN controller keeps track of the network view and the hierarchical structure of received statistics in a hash table. The computed alternative paths are also classified by the SDN controller using the network path congestion index for each path. The estimated network path congestion index of alternative paths is inversely proportional to the number of alternative paths installed by the SDN controller over OF switches. Here, we briefly discuss the SVM, NB, RF, KNN, LR, and EV classification algorithms.d. So, now that we know what we’re trying to do, we can use the point from the positive group in the hyperplane equation to get a value greater than 0 when making predictions on the training data, which was binary classified into positive and negative groups, Mathematically:The SVM algorithm belongs to supervised hyperplane-based classifiers algorithms, which are discriminative and parametric classifiers . SupportPredictions from the negative group in the hyperplane equation would give negative value as:These hyperplane-based classifiers provide classical classification by maximizing the margin iteratively perceptron learning, Fisher linear discriminant analysis, and least-squares optimization. We utilize the hyperplane-based classifiers of SVM and Logistic Regression for classifying the SDN-enabled IoT network traffic to identify the DDoS attacks. Mainly, the SVM classifier learning model trains the model by using multiple kernel functions of polynomial functions, radial basis functions. In the classical setting of SVM, from a set of training data points, the SVM algorithm represents them in a space; it maps them by categories and divides them by the separating hyperplanes. The decision boundary is maximized; when new data points arrive based on the nature of the point, it categorizes the data into the clusters previously formed. Thus, the SVM algorithm can successfully differentiate the exact nature of the flow of traffic in both normal and DDoS scenarios .y and input vector values, as y denotes class and x denotes input values, y given the data x, x given that the hypothesis was true, y, and x.NB works on the principle of independent variable comparison and finds the relationship between these independent variables . This MLCompared to advanced methods, Naive Bayes classifiers can be extremely fast. Each distribution can be estimated independently as a single dimension distribution by decoupling the class-conditional characteristic distribution. On the other hand, this solves problems caused by the dimension curse. NB classier performs efficiently for DDoS detection purpose .x will be:LR uses the logistic function to model the binary dependent variable for statistical computations. Many complex extensions of LR have been developed, but the scope of this paper is limited to regression analysis of estimating the binary regression logistic model . In the ieved by .kNN is a supervised classification and regression algorithm. The kNN algorithm’s input format is the k nearest training samples in the feature space. Whether kNN is used for classification or regression determines the outcome. This research work concentrates on traffic classification and detection of DDoS attacks. An object is categorised based on a majority vote of its neighbors, with the object being allocated to the class with the most members among its k-nearest neighbors. kNN is a type of instance-based learning, also known as lazy learning, in which the function is only approximated locally and all computation is postponed until after the function has been evaluated. Normalizing the training data can dramatically improve the accuracy of this algorithm, which relies on distance for classification. For DDoS detection, an SDN-enabled network recently used the kNN classifier .Random decision forests, or RF classification, is an ensemble learning method for classification, regression, and other applications. At training time, RF constructs a large number of decision trees and outputs the class that is the mode of the classes (classification) or the mean prediction (regression) of the individual trees. Decision trees have a tendency to overfit their training set, which is corrected by random decision forests. In , anomalyEV is a voting classification model which combines the multiple classifiers models into a single model, which is  stronger than any of the individual models alone. While building a model of ensemble voting, classifier voting was set to “Hard”. Each classifier votes for a class in hard voting, and the class with the most votes wins. Each classifier in soft voting assigns a probability value to each data point that it belongs to a specified target class. In , ensemblIn The proposed AMLSDN framework’s design is presented in this section. When the network is turned on, the hello messages are exchanged in particular. Those hello messages contain feature-request and feature-reply of OpenFlow protocol exchanged between the SDN controller and OF switches. This exchange procures the overall view of the network in the periodic interval, which is about 30 seconds. Besides, the exchanged hello messages also contain the packets of link layer discovery protocol (LLDP) that depict the overall network topology at the forwarding plane. In the proposed framework, the SDN controller and OF switches are leveraged with the OpenFlow protocol; therefore, to command the network communication, the SDN controller transmits the During the actual communication phase, the different hosts generate network data packets. When data packets from a network flow arrive at the OF switches, the OF switches search their forwarding tables for a matching entry. If a match is detected, the switch forwards the data packet according to the flow table entry’s appropriate action. Otherwise, the switch instructs the controller to calculate the flow’s action. The OF switches are connected with active communication flow and deliver the network topology of all connected OF switches based on the pair of source and destination hosts The active group OF switches shares the flow and port status, including hosts information to controller OFP_PACKET_IN and OFP_PACKET_OUT exchange. The actual network communication can also be generated by the external network source connected through the internet. In this case, OF switches are connected to the core network routers and update the SDN controller about newly arrived network packets. DDoS attacks can be generated within the SDN-enable network domain or by the external network in the form of an individual or grouped (botnet) DDoS attack. To design the DDoS detection and mitigation system for designed network architecture, we divide the functionality of the proposed AMLSDM framework into the following four phases; (i) training the adaptive classification model, (ii) feature extraction of SDN-enabled IoT network traffic phase, (iii) classification of real-time network traffic for DDoS detection phase, and (iv) DDoS mitigation phase.For the development of the adaptive classification model, we utilize the Ensemble Voting (EV) polling system to combine the classification measurement SVM, NB, kNN, LR, and FR machine learning classifiers in a multilayered feed-forwarding manner. At the first layer of the adaptive classification model, the designed module imports SVM, NB, kNN, LR, and FR from Scikit-learn machine learning algorithms python library. The output of the first layer becomes the input for the second layer of the feed-forwarding classification approach. At the second layer, we import the polling classification algorithm of EV, which accumulates the results of trained SVM, NB, kNN, LR, and FR classifiers. After finalizing the adaptive classification model, the adaptive classification module imports the data from an environment-specific dataset. The Pandas library preprocess the data into labeled tabular form and, by utilization of the sklearn.model-selection, split the dataset into the training set and testing set. Then, proposed adaptive classification module initializes the fitting of SVM, NB, kNN, LR, FR, and EV to the training dataset. The EV Classifier is a meta-classifier that uses majority or plurality voting to classify comparable or conceptually different machine learning classifiers. In this paper, we utilize the majority voting of different machine learning classifiers to implement EV Classification. Majority voting based EV classification is further categorized to hard and soft voting classifications. Hard voting entails adding up all of the forecasts for each class label and guessing which one will receive the most votes. Soft voting entails adding up the anticipated probabilities (or probability-like scores) for each class label and predicting the one with the highest probability. In majority voting, EV predicts the class label In our case, if the classifiers of SVM, NB, kNN, LR, and FR computes the 0, 1, 0, 1, and 1 values, respectively, then EV predicts the class label as:L is set of labels. In a weighted ensemble, which is an extension of a model averaging ensemble, the contribution of each member to the final forecast is weighted by the model’s performance. The model weights are small positive numbers, and the sum of all weights equals one, reflecting the percentage of trust or projected performance from each model. Because the weights are uniform, the weighted ensemble functions as a basic averaging ensemble. The weights can be calculated using either each classifier’s rate of accuracy or a holdout validation dataset; there is no analytical approach. As a result, classifiers with a higher accuracy ratio will be favored.Simple majority computed by mode most often provides an accurate result to compute class labels. In order to optimize the majority classification of EV, we can optimize the weight If we assign the The weighted majority classification of adaptive EV provides optimized class labeling for adjustment of prediction according to the characteristic of network traffic. We assign the In AMLSDM framework, the real-time network traffic generated by Mininet network topology experience performance variations after altering the network parameters. We show two different SDN network topologies in Our feature extraction module executes a shell file to collect the traffic statistics over the OF switches. It produces the “SVC” data files of Number of Packets, Size of Bytes, Number of source IPs, and destination IPs. These files are then utilized as run-time input for the designed Python script module, which computes the Rate of Source IP (RSIP), Standard Deviation of Flow Packets (SDFP), Standard Deviation of Flow Bytes (SDFB) , Rate ofT is the sample time, which can be changed depending on the SDN controller’s ability to handle the traffic flow.Rate of Source IP (RSIP): For a given destination IP address, this function displays the number of source IPs per unit of tim:T periodw’s standard deviation for the number of packets:n is the number of active network flows, T period, and T period, and T periods. This feature has a strong link to the occurrence of a DDoS attack because, during an attack, the attacker transmits a large number of attack packets with a small size; these packets will have a significantly smaller standard deviation than typical data packets, resulting in a considerable drop in this parameter.Standard Deviation of Flow Packets (SDFP): This is the T period’s standard deviation:T period, while T period. SDFB, like SDFP, has a strong link with the occurrence of a DDoS attack, and the expected value of this parameter is lower during an attack than during normal traffic flows.Standard Deviation of Flow Bytes (SDFB): This is the number of bytes in the Rate of Flow Entries on Switch (RFES): This is the number of flow entries to the switch per unit of time:Because the number of flows increases dramatically in a set interval of time during an attack compared to the SFE value during regular traffic flows, this is an important parameter for attack detection.T period divided by the number of interactively divided flow entries in the switch:N is the total number of IPs and IntIP is the total number of interactive IPs in the flow. As a result, as soon as the attack begins, the number of interaction flows will drop dramatically.The Ratio of Pair-Flow Entries on Switch (RPFES): The total number of flows in the After the computation of the above features, the feature extraction module creates the header of RSIP, SDFP, SDFB, RFES, and RPFES and assigns the computed values. On behalf of the calculated values, the feature extraction module labeled the normal traffic as 1. Finally, the feature extraction module creates the training data file of “live.csv” for the newly arrived network traffic to accommodate the real-time network traffic. This file “live.csv” only keeps the entry of newly computed network traffic and serves as the input to the trained adaptive machine learning model. These five network traffic features, Rate of Source IP (RSIP), Standard Deviation of Flow Packets (SDFP), Standard Deviation of Flow Bytes (SDFB), Rate of Flow Entries on Switch (RFES), and the Ratio of Pair-Flow Entries on Switch (RPFES), are efficient enough to differentiate between DDoS attacks and normal traffic. According to these features, DDoS attacks show abnormality in an exponential increase in the values of Rate of Source IP (RSIP), Standard Deviation of Flow Packets (SDFP), Standard Deviation of Flow Bytes (SDFB), Rate of Flow Entries on Switch (RFES), and the Ratio of Pair-Flow Entries on Switch (RPFES). In this way, DDoS detection becomes much more accurate and efficient.T, and the destination host is unable to respond to network traffic requests. As a result, as soon as the attack begins, the number of interaction flows will drop dramatically. To make this detection parameter scalable to the network under various operating conditions, the total number of interactive flows is divided by the total number of flows. By using these parameters, the AMLSDM framework utilize the trained adaptive machine learning model and continues the inspection of incoming flows to classify the DDoS attacks from normal network traffic. This DDoS detection is being made over the third layer of the proposed multi-layered feed-forwarding framework.In this phase, we implement our designed inspection module to classify the network traffic being arrived at real-time data entry of live.csv. This inspection module imports all the python libraries needed for trained adaptive machine learning models and live.csv. After importing all required libraries, it loads the trained adaptive machine learning model and real-time network database of live.csv. Finally, this module predicts the results from incoming data eateries. The computed features are combined to determine if an interaction flow is normal traffic or under DDoS attack. In DDoS attacks, the number of flow entries to a certain destination host increases dramatically over time Our proposed AMLSDM framework executes the inspection modules of classifications overall active OF switches and detects the DDoS attacks actively for run-time traffic. The OF switches update the SDN controller periodically about flow-tables and traffic rate over ingress and egress active posts to report the detection DDoS attacks. The remote SDN controller executes the built-in congestion control mechanism module to mitigate the DDoS attacks. The remote SDN controller commands the active OF switches to drop the specific communication request and switch the network resources to the legitimate network hosts. OF switches follow the instructions of the SDN controller and discard the traffic request of specific source hosts after investigated as DDoS attacks. In this way, the network resources are switched to legitimate network users to carry on the normal network traffic.The operation of the above four phases results in the execution of designed four modules to achieve the DDoS detection and mitigation of the proposed AMLSDM framework. The detailed flowchart and work mechanism of the proposed AMLSDM framework is shown in Different forms of normal traffic were mixed in with the attack traffic, and the parameters of the attack flow were varied. Oracle VirtualBox, Mininet, Mininet-wifi, Open vSwitch, POX Controller, Floodlight Controller, Miniedit, tshark, and sFlow-RT are among the applications that must be installed. We run the Pox and Floodlight controller at Virtualbox and run the sFlow-RT on our host Linux machine to capture the network traffic generated by Mininet SDN network topologies. We design a Python script to develop the adaptive classification module to integrate all the required libraries and packages to build a trained adaptive classification model. This module uses core Numpy and Pandas Python libraries for data processing. For the execution of real-time Mininet network topology for network simulation, this module also imports the default-timer and DateTime libraries. For data visualization of simulation performances, it also imports matplotlib.pyplot library. The adaptive classification module also needs performance metrics of Classification Report, Accuracy Score, Confusion Matrix, Recall Score, Precision Score, and F1 Score Python libraries to show the outcomes of the classification.We launch the threat model of DDoS described earlier and monitor the performance variation by sFlow-RT. Then we launch our machine learning-based inspection model as collect.sh, which classifies the network traffic as normal or DDoS attacks and mitigates the effect by intelligent resource distribution towards legitimate hosts. We validate our proposed framework with extensive simulations experiments, but for this paper, we report the simulation results of SDN-enabled IoT network topologies shown in The proposed AMLSDM framework leverages an environment-specific dataset on which the evaluations are performed. The network statistics are converted to a “CSV” file in this environment-specific dataset. The terminal-based program named “tshark” is used to generate that CSV file with limited fields. Moreover, the above-mentioned dataset is further categorized into traffic and attack datasets which are shown in the following We simulate the AMLSDM framework for two different MININET network topologies with POX and floodlight SDN controllers, respectively. The proposed AMLSDM framework performs the DDoS detection and mitigation with an adaptive machine learning classification model with support of the voting polling system of EV, which combines SVM, NB, kNN, LR, and RF.Similarly, We study the features of the resultant traffic using our simulations, which are displayed in the accompanying figures, to inspect typical IoT traffic and anonymous DDoS attack traffic. The incoming traffic stream was successfully separated towards the sFlow database using pre-defined traffic features of expected normal and attack traffic. The size of normal traffic packets versus attack packets is depicted in the We perform extensive simulations of the AMLSDM framework with an EV trained model and also simulate the AMLSDM framework with a stand-alone trained model of SVM, NB, kNN, LR, and RF to compare the performance enhancement of adaptiveness of the proposed framework. We refer to these frameworks as AMLSDM-EV, AMLSDM-SVM, AMLSDM-NB, AMLSDM-kNN, AMLSDM-LR, and AMLSDM-RF. We capture the classification measurements of all frameworks for SDN-IoT network topology of Mininet for lengthy simulation periods to test 5000, 1000, 15,000, 20,000, 25,000, and 30,000 network flows entries in the “live.csv” dataset. DDoS detection dashboard of sFlow returns the periodic performance metrics of Accuracy, Precision, Recall, and F1 Score. These performance metrics are derived from the following factors; True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). The following equation computes the accuracy:With higher accuracy, higher precision is also critical to understand the consistency of the designed framework. F1 Score is the harmonic mean of Precision and Recall and correctly identifies the False Positive rate. In this paper, we proposed an AMLSDM framework based on an adaptive machine learning classification model to detect DDoS attacks for network traffic of SDN-enabled IoT. The AMLSDM framework also provides a DDoS mitigation system to switch network resources to normal network traffic. The multilayered feed-forwarding design of the AMLSDM framework utilizes SVM, NB, kNN, LR, and FR classifiers in the first layer. The output of the first layer is provided as input to the second layer EV, which accumulates the performance of first layer classifiers to detect the DDoS attacks. The trained adaptive machine learning model predicts the DDoS attacks for real-time network traffic at the third layer. We implement our proposed framework in four phases; (i) training the adaptive classification model, (ii) feature extraction of SDN-enabled IoT network traffic phase, (iii) classification of real-time network traffic for DDoS detection phase, and (iv) DDoS mitigation phase. In every phase, we execute the adaptive classification module, feature extraction module, DDoS inspection module, and DDoS mitigation module. Our extensive simulation results validate the intelligent DDoS detection and mitigation of the AMLSDM framework to classify the real-time network traffic generated by two SDN-enabled IoT networks. Performance metrics of Accuracy, Precision, Recall, and F1 Score validate the better classification of adaptive setting AMLSDM-EV as compared to static AMLSDM-SVM, AMLSDM-NB, AMLSDM-kNN, AMLSDM-LR, and AMLSDM-RF classification configurations. We also perform the simulation comparison with state-of-the-art LEDEM and CONA frameworks to validate the better performance of the AMLSDM framework.In the future, mitigation of DDoS attacks at SDN controller will be explored more deeply, as it is one of the key research area to further improve the utilization of SDN controllers in IoT networks. We will also extend the implementation of our proposed framework to detect phishing attacks.


Graph neural networks (GNNs), which work with graph-structured data, have attracted considerable attention and achieved promising performance on graph-related tasks. While the majority of existing GNN methods focus on the convolutional operation for encoding the node representations, the graph pooling operation, which maps the set of nodes into a coarsened graph, is crucial for graph-level tasks. We argue that a well-defined graph pooling operation should avoid the information loss of the local node features and global graph structure. In this paper, we propose a hierarchical graph pooling method based on the multihead attention mechanism, namely GMAPS, which compresses both node features and graph structure into the coarsened graph. Specifically, a multihead attention mechanism is adopted to arrange nodes into a coarsened graph based on their features and structural dependencies between nodes. In addition, to enhance the expressiveness of the cluster representations, a self-supervised mechanism is introduced to maximize the mutual information between the cluster representations and the global representation of the hierarchical graph. Our experimental results show that the proposed GMAPS obtains significant and consistent performance improvements compared with state-of-the-art baselines on six benchmarks from the biological and social domains of graph classification and reconstruction tasks. Over the past years, deep neural networks have achieved great success in various tasks such as computer vision , naturalAlthough the majority of the existing work focuses on the design of convolutional operations, graph pooling, which maps nodes into a coarsened graph, is critical for capturing hierarchical structural information in graph classification tasks. However, unlike the pooling operations in traditional CNNs, graph pooling operations are challenging due to the fact that graphs usually contain diverse irregular topologies and no explicit spatial sequence of nodes . A simplQ and K regarding the attention mechanism, which constructs the structure of the coarsened graph. In addition, to ensure the expressive ability of cluster representations, we introduced a self-supervised mechanism [Q and K in the attention model to generate the topology of the coarsened graph.We considered the graph pooling as a node clustering assignment problem. For accurate clustering assignment, a multihead attention mechanism based on GNN was introduced to sufficiently consider the connections between nodes in terms of features and the structure. Then, we derived Self-supervised learning was adopted to maximize the mutual information between the cluster representations and hierarchically global representations, which further optimized the node representation in the coarsened graph.Finally, the experimental results showed that the proposed GMAPS significantly outperformed baseline methods in the graph classification task on six publicly available datasets from the biological and social domains.In this work, to obtain an accurate representation of the graph, we needed design a graph pooling method that can compress and encode the node set into a coarsened graph, and construct the coarsened graph structure. To this end, following DiffPool , the graechanism ,28 to maThe rest of this paper is organized as follows: In K-order polynomials of graph Laplacian. Later, ChebNet [K-localized convolution to define a convolutional neural network on graphs. The graph convolutional network (GCN) [K-localized convolution to Existing GNNs models generally utilize the message-passing strategy  to encod ChebNet  used thirk (GCN)  further h layers . To joinh layers  extendedh layers  proposedh layers  incorporh layers ,36,37. Hh layers ,39,40.Graph pooling is an essential unit in the hierarchical graph representation learning task, which captures node features and the hierarchical graph structure. Directly averaging or summing the node representations of the entire graph is the simplest pooling operation; however, it ignores the diverse weight of nodes and the hierarchical graph structure . ExistinThe key idea of the proposed GMAPS is that it enables the construction of graph pooling through a differentiable node assignment based on a multihead attention mechanism and a hierarchical objective based on maximizing mutual information. In this section, we present the overall GNN architecture and show the details of each module.G is represented as n and l denote the number of nodes and edges, respectively. Let f is the dimension of node features. Given a set of labeled graphs kth layer as d is the dimension of any hidden representations in neural networks, and k.A graph kth layer. The aggregation function GNNs learn node representations through various aggregation schemes, which are generally described as the following message-passing architecture :(1)Hk+1=There are many possible implementations for the GNN architecture, such as GCN , GraphSAkth pooling layer as kth layer nodes GNNs are inherently flat, as they only propagate information across the edge of a graph. The goal of graph pooling is to define a differentiable end-to-end operation to generate the structure and the representation of a coarsened graph for hierarchical GNN models. Following Ying’s strategy , the poo follows:Hk+1=Sk⊤HRnk+1×d,Ak+1=Sk⊤AExisting methods ,25 generLet ttention  functionIn this paper, queries d-dimensional queries, keys, and values, we can linearly separate the queries, keys, and values with h different learned projections function into Furthermore, instead of performing a single attention function with k, the inputs of keys and values in the multihead attention can be generated as follows:There is a challenge in directly implementing the above multihead attention mechanism into the graph pooling operation: while the multihead attention mechanism considers the internal connections between nodes, the linear projection of keys and values still inhibits the improvement in the attention. For this reason, inspired by DiffPool , we emplNote that the GNN models for keys and values can be employed based on specific tasks, and GCN , GraphSABased on the above ingredients, graph multihead attention (GMA) can be formally expressed as follows:In addition to GMA, following the transformer model architecture, each of the proposed pooling layer contains a fully connected feed-forward network (FFN), which is separately and identically applied to each row, followed by layer normalization. Thus, the overall architecture of graph multihead attention pooling (GMAP) can be formally described as follows:Note that the output of GMAP is the embeddings of the clusters in the coarsened graph, which serve as the node embeddings of the next graph convolutional layer. After compressing the nodes of the original graph into clusters, which are nodes in the coarsened graph, we need to reduce the adjacency matrix of the original graph to another refined adjacency matrix of the coarsened graph.While a coarsened graph can be constructed by compressed clusters, the connections between nodes in the coarsened graph still need to be generated by the node clustering assignment matrix. We first consider the formation for node clustering assignment matrix by the attention function as in Equation . In thisEquation  can be dNote that compared with the cluster assignment directly generated by GNNs in DiffPool , our proFor each pair of nodes in the coarsened graph, the edges are constructed by considering the edges between the corresponding clusters in the original graph. Therefore, we can formally construct the adjacency matrix of the coarsened graph as follows:Our pooling method, GMAP, can achieve hierarchical representation of the original graph. To discriminate the cluster representations in the hierarchical graph, the self-supervised learning mechanism is utilized to maximize the mutual information between the cluster representation and the hierarchically global representation.kth layer graph.To generate the global representation of the hierarchical graph, we leveraged a readout function to summarize the node representation of the hierarchical coarsened graph into a fixed size graph level representation as follows:i, and As a proxy for maximizing the mutual information between the cluster representations and hierarchically coarsened graph, a discriminator function is employed to measure the probability scores that represent the clusters contained within the hierarchical coarsened graph. We formally show the discriminator function as follows:For the self-supervised mutual information objective, we followed the intuitions in ,43 and eThe space complexity of each graph pooling layer is To evaluate the performance of the proposed pooling model, GMAPS, GMAPS was compared with a collection of state-of-the-art GNN-based models on six benchmarks in terms of graph classification tasks. Furthermore, detailed ablation and parametric analyses were conducted to characterize the proposed pooling model.https://chrsmrrs.github.io/datasets/docs/datasets/ (accessed on 22 June 2022). Statistics and properties are summarized in We adopted six public benchmark graph classification datasets among the TUDatasets . The datGMAPS was compared with state-of-the-art GNN-based models that could be categorized into two groups: global graph neural networks and graph pooling models.Global graph neural networks include three representative models: GCN , GraphSAFor the graph pooling models, we deployed the same hierarchical GNN architecture, replacing only the pooling methods in it. To evaluate the performance of the proposed pooling model, gPool , ASAP 4, SAGPoolFinally, to further analyze the performance of the proposed pooling models, we introduced two variants: GMAP (Equation ) condensFollowing many previous studies ,45, for For all datasets, the learning rate was set to The results of graph classification are reported in For global GNN baseline methods without hierarchical pooling, such as GCN, GraphSAGE, and GAT, their overall performance was weaker than that of the hierarchical graph pooling approaches, especially on D&D and MUTAG datasets. For such bioinformatic networks, the structure of the subgraphs therein often represents different properties of the molecule and is crucial for learning the graph representations. Global GNN baseline methods only globally summarize the node representations and ignore the graph structural information. Thus, they struggle to achieve satisfactory performance.Among the hierarchical graph pooling models, DiffPool achieved superior results in most cases, while the other baseline methods had their own performance on various datasets. We argue that the major reason is that their respective heuristic node selection strategies are only applicable to specific scenarios and are not versatile. Specifically, DiffPool utilizes an auxiliary link prediction objective during training to cluster the nearby nodes. In contrast, our proposed model considers both the graph structure and the internal connections between nodes; the satisfactory performance on different datasets further validates the generality of GMAPS. Note that the performance on D&D, PROTEINS, and MUTAG showed an impressive improvement over that of the global baseline method, while the performance on IMDB-B, IMDB-M, and COLLAB was similar to that of the baseline method. This may be due to the fact that the latter three social network datasets lack raw features and encode node degrees as features.Finally, among our proposed models, GMAPS, which employs self-supervised mutual information maximization, consistently outperformed GMAP, which indicated the mutual information maximum module is facilitative for graph pooling in the graph classification task.X and the reconstructed features To quantity the amount of information about the graph retained by different pooling methods, we trained an autoencoder to reconstruct the input node features from the pooled representations. The learning objective to minimize the mean squared error between the original feature posed by . We plotAs mentioned above, our proposed pooling models can be integrated into various GNN architectures. To explore their performance with different graph convolutional strategies, we implemented GraphSAGE and GAT, except for the default GCN convolutional layer. The experiments were conducted on the D&D, PROTEINS, and MUTAG datasets, and the results are presented in S is presented in To further explore the clustering assignment process of the pooling model, a visualization of the clustering assignment matrix d and pooling ratio r) on the pooling model, GMAPS, by considering the PROTEIN, MUTAG, and IMDB-BINARY datasets with various settings. r is presented in We further investigated the effect of two hyperparameters (dimension In this study, we found that existing graph pooling methods either cause the unnecessary loss of node features or do not adaptively learn an accurate graph representation in a specific downstream task. To address these limitations, we designed a novel graph pooling operation called GMAPS, which can compress nodes into a coarsened graph in a soft clustering manner. It also utilizes self-supervised mutual information maximization to ensure consistency between the cluster representations and the hierarchical coarsened graph representation. To validate the performance of the proposed pooling operation, we conducted experiments on six publicly available datasets and compared the results with those of seven state-of-the-art baseline methods. The results of graph classification and graph reconstruction tasks showed the excellent performance of the proposed model. Considering that the proposed pooling approach is generally applicable to various graph-learning tasks, which are growing more crucial, we are certain that it will have a significant practical impact. In the future, we will attempt to employ local subgraphs or anonymous random walks to capture the coarsened graph’s structure for more accurate pooling operations because improved local subgraph property extraction can improve the coarsened graph’s accuracy and interpretability. The key challenge of this effort is the capture and representation of task-related local subgraphs’ properties, which will be the focus of our future study. In addition, applying self-supervised learning without contrastive pairs to graph classification tasks is another aim for future research.


Several pharmaceutical companies are nowadays considering the use of agri-food waste as alternative raw material for the extraction of bioactive compounds to include in nutraceuticals and food supplements. This recycling activity is encountering the support of authorities, which are alarmed by air, soil and water pollution generated by agricultural waste disposal. Waste reuse has several economic advantages: (i) its low cost; (ii) its abundance; (iii) the high content of bioactive molecule ; as well as (iv) the financial support received by governments eager to promote eco-compatible and pollution-reducing practices. While nutraceuticals produced from biowaste are becoming popular, products that have been risk-assessed in terms of safety are quite rare. This despite waste biomass, in virtue of its chemical complexity, could, in many cases, mine the overall safety of the final nutraceutical product. In this review, we summarize the scientific results published on genotoxicity risk-assessment of bioactive compounds extracted from agricultural waste. The review depicts a scenario where the risk-assessment of biowaste derived products is still scarcely diffuse, but when available, it confirms the safety of these products, and lets us envisage their future inclusion in the list of botanicals allowed for formulation intended for human consumption. Since 1960, modern and developed western societies have fully committed themselves to an unconscious consumeristic habit. Without a real need, capitalism has let people crave unnecessary amounts of food and food products, ultimately fostering the deleterious environmental impact caused by this massive request ,3,4,5,6.2, methane, ammonia and Nitric Oxide), water , microbes  and soil pollution [Biowaste reaches, globally, a billion tons per year, and it has been estimated that more than 70% in weight of the starting biological material is disposed of. Biowaste is mainly composed by unprocessed agricultural by-products  as well as by 2nd-best food products, whose exterior appearance does not fulfil the aesthetic requirement imposed by the globalized modern market ,6. Biowaollution .The World Health Organization considers waste minimization as a mandatory task for modern society. In 2014, the European Food Safety Authority (EFSA) referred to reduction of biowaste pollution as a necessary global community effort, to be achieved thanks to the development of harmonized eco-compatible procedures for waste disposal and recycling [While the recycling of waste biomasses for production of animal feed and biofuel ,10 is noConsidering the low cost of agricultural biowaste and the high amount of contained biomolecules, it is easy to understand why an increasing number of pharmaceutical companies are investing in biowaste recycling and why an equal number of scientific publications describe recycling procedures, as well as composition, health benefits of final products obtained from biowaste ,23.While the scientific community should keep on fostering research on biowaste reuse, an issue on the safety of biowaste should necessarily remain open. Are biowaste-derived products safe for humans?monitus of this comment is that the obvious long-term environmental benefit of biowaste recycling could be undermined by its improper or incomplete risk-assessment.EFSA, at the light of the consultations of the panels Biological Hazard (BIOHAZ)  and AnimRegarding the reuse of biowaste for nutraceutical formulations, two aspects of biowaste cast doubts on the safety of its derived products and their use for human consumption. The first relates to the chemical complexity of the biowaste. The technology used to process this material (often harsher as a consequence of the hardness of the plants’ parts used) might cause the release in the final product of chemical species potentially harmful to human health. Similarly, the use of the non-edible parts of the plant  could expose the consumers to toxins produced either by yeast or fungi, like aflatoxins B1. Environmental pollutants originating from pesticides and fertilizers could also represent the source of genotoxic compounds  . FinallyThe second concern about biowaste reuse relates to the eventual change in the absorption, distribution, metabolism, and excretion (ADME) profile of the final product that cannot in any way be considered identical to that obtained by traditional non-biowaste sources. Unexpected molecules could influence, synergistically, the bioactive fraction of the plant and alter the overall pharmacokinetic parameters of the final product  as well as its overall toxicity.In 2009, the EFSA published the guideline entitled “Safety assessment of botanicals and botanical preparations intended for use as ingredients in food supplements” ,27. The Salmonella Typhimurium and Escherichia Coli) to a test substance. The bacterial strains used in the Ames test present mutations in genes needed for the synthesis of a specific amino acid . Differently from wild-type (wt) strains, these bacteria have lost auxotrophy for those amino acids and cannot grow in their absence [The Ames test  identifi absence . Genotox absence .The purpose of the MNvit test  is to idThe single cell gel electrophoresis test, also known as Comet assay (OECD guideline 489) , is a siLike the MNvit, chromosomal aberration assay (CAA) is used to identify substances that may cause structural chromosomal and chromatid aberration in metaphase cells. The test can be conducted both in vivo and in vitro, according to the OECD 475  and OECDWhile the rationale behind the EFSA’s suggestion to confirm the safety of nutraceutical products and the risk-assessment procedures is clear, how many products obtained from biowaste and presented by the scientific community as intended for human consumption have been risk-assessed?In this review, we collect and discuss manuscripts (full and short articles and letters) reporting risk-assessment of biowaste products. In order to identify products that underwent at least the first group of in vitro tests suggested by EFSA, we focused on genotoxicity risk-assessment. The search of PubMed Medline (performed in May 2022) with the keywords “food waste ” AND “genotoxicity ” retrieved more than 150 results describing products obtained from agricultural waste and destined for human consumption, of which only 24  present Laeis guineensis and Moringa oleifera. Its production generates a large amount of waste (corresponding to 90% of the harvested fruits) and represents a serious environmental concern. Among palm oil by-products, the black liquor waste (BLW) contains lignin, a bioactive compound with health-promoting properties including antimicrobial, anti-parasitic, antioxidant, anticancer, genoprotective and immune protective activities [Palm oil, the main cooking oil in non-European countries, can be obtained from the fruits of several species of palms including tivities , that gativities  tested bOlea Europea cultivation occupies about 10 million hectares worldwide. Food industries mainly use its fruits, while leaves and flowers are not commonly used, although they present high amounts of phenolic acids, anthocyanin and flavonoids [Salmonella Typhimurium strain TA102).Like palm oil, olive oil production generates by-products, the accumulation of which may represent a source of pollution and are of interest for pharmaceutical industries. avonoids . Kouka aavonoids  analyzedSalmonellaTyphimurium strains TA98 and TA100) with negative results registered up to 500 µg of Gallic Acid equivalents/plate [SalmonellaTyphimurium strain TA100 up to 5.0 µL per plate) and CAA test [Drosphila Melanogaster wings. The product was not shown to be genotoxic up to the doses of 30 µL/mL [In addition to leaves and flowers, three other types of waste are produced during olive oil manufacturing processes: olive mill wastewater, alperujo, and olive brine. These by-products contain several bioactive compounds including hydroxytyrosol, a compound endowed with antioxidant activity . The gents/plate . SimilarCAA test . AlperujJuniperus communis are berries and essential oil. The essential oil production generates a by-product known as post distillation water. Such a by-product is rich in polyphenols, including rutin, quinic acid, catechin and epicatechin. Vasilijević and colleagues [Nonedible oil can also produce biowaste. The commonly used parts of lleagues  evaluateCamellia sinensis leaves, while flowers  are wasted [Salmonella Typhimurium strains TA97, TA98, TA100 and TA102), both in the absence or in the presence of S9 metabolic activation up to the doses of 5.0 mg/plate [The second most drunk beverage in the world after water, tea, has a waste production reaching about the 4% of the entire production. The beverage is obtained by infusion of e wasted . Tea biomg/plate .Salmonella Typhimurium TA98 and TA100 strains) both in the presence and in the absence of metabolic activation by S9 at doses up to 5 mg [Rice is one of the most consumed food worldwide. Its processing produces rice hush as biowaste, that represent about 20% of the rice processed. Thanks to its high content of phenolic (mainly anthocyanin) and non-phenolic compounds, numerous properties have been attributed to rice husk, including anti-inflammatory, antioxidants, anticancer activities . Rice hu to 5 mg ,56.Citrus reticulata (rich in nobiletin and tangertin) by Ames test  and MNvit tests [Citrus are the most widespread and consumed fruits in the world. Their production results in a large amount of waste, mostly peels, highly enriched in bioactive compounds. As consequence of the low cost of these wastes, the interest of industries for these by-products has recently grown. The genotoxicity of citrus peels  was tested on mouse splenocytes through the MNvit test, with negative results . Similarit tests . A minimSalmonella Typhimurium TA100, TA98, TA1537, TA1535 and EscherichiaColi doses up to 5 mg/plate), in both cases giving negative results [Like citrus, apples are highly appreciated and consumed fruits in the world, and possess several health-promoting properties, including prevention of cardiovascular and chronic diseases ,60. The  results .Avocado is largely consumed in Latin America. Due to its abundance in phytosterols, triterpenes, fatty acids, and two new glucosides of abscisic acid, various beneficial effects have been referred to the consumption of this fruit, including antioxidant, antihypertensive and hypolipidemic properties . InteresCyphomandra betacea also known as tamarillo, or tree tomato, usually grows in south America and New Zealand. The ripe fruits are commonly used in the food field in the preparation of dishes. Ordóñez and colleagues confirmed the non-genotoxicity of an insoluble fraction of tamarillo pomace by the Ames test  [ivation) .Lansium domesticum is a widely diffused fruit in Thailand, where is known as long-kong. However, only the pulp is consumed, while peel and seeds are wasted despite their high content of scopoletin, chlorogenic acid and rutin. The genotoxicity of this biowaste was assessed by Comet assay by Klungsupya and coworkers [oworkers , who conPunica granatum L.), mainly composed by the peel of the fruit, enriched in tannins  and flavanoids  was shown non-genotoxic and genoprotective by MNvit and CAA assay [An extract of Pomegranate waste  is a well-known and expensive spice that is obtained from dried stigmatic lobes of the pistil. The production of saffron results in a large production of waste materials. Of particular interest are the petals and anthers. These parts of the flowers, indeed, are rich in substances such as crocin and kaempferol, useful for the preparation of functional food and cosmetics [Saffron  [Winemaking industry generates a large amount of waste , that still results rich in bioactive compounds, mainly polyphenols, including stilbenes, phenolic acids, flavonoids, tannins and oligomeric procyanidins . The absg/plate) . Grape sg/plate) . Grape pg/plate) .United States are one of the largest producers of soybeans (around 32% of the world production) and fulfill the growing demand for this agricultural product. Such huge production is accompanied by the accumulation of by-products that are usually discarded despite being rich in bioactive compound (mainly isoflavones) . Plewa aOnion is one of the most extensive crops in Europe and England and presents an associated waste production of about 50,000 tons per year. Onion by-products are rich in fructo-oligosaccharides with potential antioxidant and prebiotic proprieties. This biowaste was found to be non-genotoxic by the Comet assay on rat’s liver and leucocytes .Crataegus oxyacantha L. is a deciduous tree member of Rosaceae family commonly known as Hawthorn. The uses of many parts of the plant, i.e., leaves, flowers, and unripe berries have been described for their traditional use . Recently, the barks, one of the by-products of its processing, are under investigation for their nutraceutical properties. Aguilera-Rodríguez and colleagues [lleagues  evaluateAs a consequence of the expected increase in the world population , the proAligned to the effort of the general community, science is contributing to this global mission by fostering research aimed at the valorization of biowaste, including its reuse for pharmaceuticals and nutraceuticals purposes. Among the scientific issues, and as suggested by authorities like EFSA, science must address the safety of biowaste reuse. We have here reviewed scientific articles describing risk-assessment of products obtained from biowaste and intended for human consumption. At the end of this survey, we can conclude that compared to the number of nutraceuticals produced form waste, still too few have been risk-assessed.Genotoxic risk-assessment of bioproduct obtained from agricultural waste has to represent, of course, only a first step in the confirmation of the their safety . The risbiological = safe” is not an ultimate truth and pretend that the products they consume are safe. They are supported by authorities, that, from a regulatory point of view, advise developers and producers to act responsibly and confirm the safety of their products by showing their compliance with the safety requirements set out in the general food law. Despite this advice, the lack of a firm regulation has allowed the setting of a “foggy landscape” in which developers and producers of biowaste-derived products move autonomously. Many companies dispense themselves from risk-assessment as they use as starting material the biowaste of plants included in the national compendium of allowed botanicals. However, in the case of biowaste, the lists do not clearly cite biowaste among the biomass to include in the final products. More importantly, in case of an incurring safety issue (intoxication or unexpected side effects), the fact to have used products included in a list of allowed botanicals will not add any force to an eventual legal defense.The lack of safety confirmation for food products obtained from waste is very risky and might mine the overall market and the investments that industries are making in this important recycling practice. The public of consumers is acquiring a growing culture on the health properties of natural products . Consumers are nowadays aware that the paradigm “What could further convince companies to risk-assess is that, in the light of the results so far presented by the scientific literature (here reviewed), products obtained from biowaste are in most cases safe, at least in terms of genotoxicity. The biosafety of the extracts obtained from traditional sources indeed seems to also be maintained when the bioactive fractions are extracted from biowaste. Some of the extracts even present genoprotective activity against known mutagens. Producers and stakeholders could thus envisage a likely future scenario where the recycled biowaste will be allowed for the preparation of nutraceuticals and the practice of recycling is further supported.


Dear Editor,Since the emergence of the first cases of coronavirus disease 2019 (COVID-19) at the end of 2019, understanding of this disease has been advancing rapidly. Updates on this subject are crucial to healthcare professionals who are engaged in the battle against this disease. However, it is noteworthy that the emergence of updates not based on evidence has generated difficulties in the clinical management of patients in the midst of this pandemic, especially when dealing with cases of misleading information.,In March 2020, the French Minister of Health (Oliver Veran) used the social network Twitter to announce that unpublished data indicated that use of nonsteroidal anti-inflammatory drugs (NSAIDs) could increase the severity of COVID-19, possibly due to viral replication mechanisms. This comment seems to have originated in part from the clinical opinion of an infectious disease specialist in southwestern France, where it was observed that four cases of previously healthy young patients with COVID-19 progressed to the severe stage of the disease after previous use of NSAIDs to combat the initial phase symptoms. This announcement had worldwide repercussions and led to clinical insecurity regarding drug therapy for patients with COVID-19, despite the lack of any scientific evidence.,,,Given this scenario, regulatory authorities have tried to investigate the association between use of NSAIDs, especially ibuprofen, and patients’ severity of COVID-19.NSAIDs are considered to be potentially inappropriate medicines (PIMs) for use among the elderly, due to these individuals’ senescence. They should be prescribed with caution, given that the risk might be greater than the clinical benefits provided, while safer and more effective alternatives may be available. Thus, they can be considered potentially inappropriate.Consumption of NSAIDs is commonplace, especially in age groups ≥ 60 years, since they are dispensed readily in pharmacies in Brazil, without any requirement to restrict medical prescriptions. This access to medications that are considered to be over-the-counter (OTC) drugs by Brazil’s national regulatory agency facilitates occurrence of self-medication, polypharmacy, drug interactions and imminent risk of adverse reactions. However, it has been recognized that use of NSAIDs potentially gives rise to health risks, through promoting salt and water retention, reducing inhibition of chloride absorption and reducing the action of the antidiuretic hormone, thus facilitating a propensity to hyperkalemia, edema and decompensation of blood pressure levels, especially in hypertensive individuals. In addition, there are risks of dyspepsia, heartburn, gastric and duodenal ulceration, increased risk of bleeding time due to disorders of platelet function, acute renal injury due to renal hypoflux and decreased glomerular filtration rate.In view of the above, it is noteworthy that, to date, there has been no significant evidence for any increase in clinical severity of COVID-19 among patients who use of NSAIDs, especially ibuprofen. Nonetheless, this scenario brings a need to review the form of prescription among patients with COVID-19 and the form of dispensing this pharmacological group.


In recent years, appreciation for the gut microbiome and its relationship to human health has emerged as a facilitator of maintaining healthy physiology and a contributor to numerous human diseases. The contribution of the microbiome in modulating the gut-brain axis has gained significant attention in recent years, extensively studied in chronic brain injuries such as Epilepsy and Alzheimer’s Disease. Furthermore, there is growing evidence that gut microbiome also contributes to acute brain injuries like stroke(s) and traumatic brain injury. Microbiome-gut-brain communications are bidirectional and involve metabolite production and modulation of immune and neuronal functions. The microbiome plays two distinct roles: it beneficially modulates immune system and neuronal functions; however, abnormalities in the host’s microbiome also exacerbates neuronal damage or delays the recovery from acute injuries. After brain injury, several inflammatory changes, such as the necrosis and apoptosis of neuronal tissue, propagates downward inflammatory signals to disrupt the microbiome homeostasis; however, microbiome dysbiosis impacts the upward signaling to the brain and interferes with recovery in neuronal functions and brain health. Diet is a superlative modulator of microbiome and is known to impact the gut-brain axis, including its influence on acute and neuronal injuries. In this review, we discussed the differential microbiome changes in both acute and chronic brain injuries, as well as the therapeutic importance of modulation by diets and probiotics. We emphasize the mechanistic studies based on animal models and their translational or clinical relationship by reviewing human studies. The micoke risk . Butyratotential . A growiotential .The purpose of this article is to review the gut-brain relationship, evidence from both animal and human studies observing changes in microbiome signatures during acute and chronic neurological injury states, and the effect of the diets like the ketogenic diet (KD) in mitigating brain inflammation and supporting brain recovery. This review has the potential of serving as an evidence base to consider the use of microbiome modulators like KD, probiotics, and others in brain recovery and expand its use in acute brain therapy.The colonization and character of the human microbiome is established at birth, with rapid development until 2 years old, after which much of the microbial composition is stabilized through adulthood with minor changes through aging . The iniFirmicutes and Bacteroidetes are suggested to represent the largest proportion of microbiome  tract to normalize the bacterial composition and correct a dysregulated gut . The gut-brain axis (GBA) can be defined as the bidirectional communication between the gut and the CNS that is integrated with multiple signaling pathways, including the enteric nervous system, sympathetic and parasympathetic pathways of the autonomic nervous system, endocrine system, and immunological system . The bidThe brain, made of billions of neurons and glial cells, maintains connections to our body and gut in a selective and specific manner in a normal state of health. The brain is covered by a highly selective microvasculature called the blood brain barrier (BBB). The BBB is normally impermeable to many large molecules, polar substances, and bacteria through its tight junctions but allows passage of certain molecules through selective transport proteins on the BBB . The BBBDuring embryonic development of the BBB, the gut microbiome is hypothesized to be a contributor  with eviThere is crosstalk between the gut and brain along the GBA to maintain homeostasis and healthy functioning. The microbiome produces several key factors that contribute significantly to neuronal and glial cell function and metabolism, such as SCFA, Tryptophan metabolites and neurotransmitters, and vitamins . The micShortly after acute brain injury, ischemic tissue will propagate significant neuronal apoptosis, necrosis, and inflammation leading to the release of immune cells, damage associated molecular patterns (DAMPs), and cytokines along the GBA . These dAcute neurological injuries such as ischemic stroke, traumatic brain injury (TBI), and intracerebral hemorrhages (ICH) are considered in this review. They each carry a high morbidity and mortality in the afflicted population. Understanding the brain-gut interactions in these acute injury states could lead to insights about possible new therapeutic interventions.via top–down processes which affects bottom-up neuroinflammatory signaling from the gut -producing bacteria  and enhanced levels of Lactobacillaceae, Akkermansia, Enterobacteriaceae, and Porphyromonadaceae in those with acute ischemic stroke  from trauma and aneurysmal rupture, intraparenchymal hemorrhage (IPH), subdural hemorrhage (SDH), and epidural hemorrhage (EDH). ICH accounts for 2.8 million deaths per year  and the To observe the effect of the GBA and microbiome on T cell regulation after ICH, a recent rodent study by Proteobacteria and Actinobacteria bacteria and reductions in SCFAs. Exacerbation of HT was not seen in mice treated with antibiotics suggesting that susceptibility to HT may be related to changes to the microbiome (Another study using a rat model noted an increase of gut permeability from onset to 7 days following ICH-induction as well as a reduction in IgA and gut barrier tight junction markers . The stucrobiome .Subarachnoid hemorrhage (SAH), is most often caused by a ruptured aneurysm . In a SAFirmicutes and depleting Proteobacteria  is a neurodegenerative disease with a state of chronic neuroinflammation and synaptic dysfunction as a primary manifestation  and its’Recent literature in both rodent and human studies have observed evidence of gut microbiome dysbiosis in advanced AD, suggesting a significant role of the microbiome in the pathogenesis of the AD .Firmicutes, Verrucomicrobia, Proteobacteria, and Actinobacteria and simultaneous increases in Bacteroidetes and Tenericutes phyla  . Anothernt model . Interesth Aβ-42 . All takth Aβ-42 .Parkinson’s disease (PD) is a neurodegenerative movement disorder due to the dramatic reduction of dopaminergic neurons in the substantia nigra. Interestingly, the pathological marker of PD, alpha-synuclein aggregates, is first detected in the enteric nervous system prior to the discovery in the brain , highligFirmicutes and increased abundance of Bacteroidetes, and OCN’s neuroprotective effect is thought to be facilitated by propionate, a SCFA , an osteoblast-derived protein, was successful in reducing dopaminergic cell loss and motor deficits in a PD mouse model through modulation of the gut; OCN depleted , a SCFA .Prevotellaceae than the levels in age-matched controls , caused by diminished blood supply to the brain due to a compromised cerebrovascular system, is the second most common cause of dementia and leads to a progressive cognitive, functional, and memory impairments .Clostridium butyricum was shown to have a neuroprotective effect against VaD in a mouse model through improvement of cognitive function, reduced apoptotic rate, and decreased histological changes in the hippocampal CA1 region (Clostridium butyricum increased levels of Brain-derived neurotrophic factor (BDNF), a potent neural mediator of inflammation and apoptosis  were reduced with glioma . Researcpatients . These tAkkermansia muciniphila, might affect the response of PD-L1, an immune checkpoint  is a progressive neurodegenerative disease that impacts upper and lower motor neurons, compromising functions such as breathing and ultimately leading to death typically within 2 years of disease onset. While some cases of ALS have been linked to genetic mutations such as ucidated . Attentiucidated .G93A mice supplemented with butyrate observed improved survival, enhanced gut barrier integrity and replenishment of butyrate-producing bacteria in the gut. Another study with SOD1G93A mice correlated 11 bacteria with disease severity, such as Ruminococcus torques and Parabacteroides distasonis, and indicated a protective effect with treatment of Akkermansia muciniphila , researchers suggest that the initiation of an endogenous immune response is dependent on commensal gut flora , and they to EAE  and human in EAE . In contn in EAE .Akkermansia  and CD80 monocytes, and increased abundance of taxa, notably Lactobacillus, Streptococcus, and Bifidobacterium species  supplementation 3-hydroxybutyrate , and acetone and the former two are shuttled into the citric acid cycle to generate ATP. Increased levels of ketones are able to “spare” glucose from being used in energetic tasks, and this action of KD has been implicated in underlying the therapeutic potential of KD on the brain  and necrosis factor (TNF), peripheral cell recruitment, and local tissue damage . Means tThe use of KD and its induced state of ketosis to treat neurological disease dates back to over a century ago, where it was implemented as a treatment for pediatric epilepsy at the Mayo Clinic in 1921 . Since tAkkermansia and Parabacteroides that, combined, restored the KD defense against seizures. Furthermore, the study demonstrated the same KD-associated seizure effect in control mice that underwent a FMT from KD-fed mice  saw, within 1 week of KD diet initiation, KD-associated changes to the microbiome included decreased alpha diversity, reduced composition of roidetes . Anotherbacteria  while anwith DRE . A studyll to KD . A receneduction . The effeduction  and warrBeyond epilepsy, KD has been suggested to be therapeutic in mitigating neuroinflammatory processes. In a rodent model of Parkinson’s disease, KD was suggested to reduce microglia activation and suppress inflammatory factors such as IL-1β, IL-6, and TNF-α . KD therRoseburia, Bacteroides, and Faecalibacterium prausnitzii (Akkermansia) comparable to healthy controls by week 24  and chemokines  . KD has usnitzii ; however week 24 . In a mo week 24 . FurtherEnterobacteriaceae, Akkermansia, Slackia, Christensenellaceae, and Erysipelotrichaceae while reductions in abundance of Bifidobacterium and Lachnobacterium were noted. Researchers linked particular microbiome changes to improved AD biomarkers, such as amyloid beta and tau proteins, in the CSF  patients attributed the modified-KD to changes in the microbiome signatures and increases in SCFA production . Bacteri the CSF . A recen the CSF . The saf the CSF . Another the CSF . The stuproteins . The stu2O2 and 4-hydroxy-2-nonenal (4-HNE), suggestive of activating the Nrf2 pathway, the primary cellular response pathway  is not addressed in this review due to the scope of brain injury, there are several parallels to the pathophysiology of TBI with SCI . In rode® caloric intake, but no impact of KD on cerebral hemodynamics was observed (® and KetoVie® are two commercially available formulas for dietary medical management of DRE epilepsy treatment and other KD-benefiting conditions, with both products consisting of a liquid diet with a 4:1 ketogenic ratio formula. Long-term compliance with KD can be challenging considering patients may opt to deter from the regimented diet for more appetizing foods. In the realm of acute brain injury, the use of Ketogenic feeds can be very promising, considering issues with compliance can be regulated in a hospital setting. While the studies mentioned prior noted effects of KD on chronic brain injury and correlated them with microbiome changes, studies in the context of KD therapy for acute brain injury do not cite any microbiome observations. Given the infancy of the study of the KD-microbiome connection, especially in the context of acute brain injury, such a gap is expected but warrants further trial.Human studies of KD and acute brain injury are very limited. Recently, a Phase II randomized control trial was done in the setting of acute brain injury with KD enteral administration . The stuobserved . The stuobserved . KetoCalEnterobacteriaceae and Proteobacteria are enhanced why mutualistic and beneficial taxa such as Faecalibacterium and SCFA-producing bacteria are relatively depleted. It can be argued that the presence of gut dysbiosis and abundance of opportunistic bacteria is linked to the exaggeration of neuroinflammation, delay in functional recovery, and aggravation of tissue damage. With our growing understanding of the link between brain injury and dysbiosis, the KD provides a promising opportunity in meeting the unmet metabolic, energetic, and immunologic demands of the brain in an acute and chronic state of injury and inflammation.Taken together, the microbiome is affected by neurodegenerative conditions as well as brain injury states. While the specific changes to the microbiomes of patients suffering different neurological disorders are varied, a common theme is presented, in both acute and chronic brain injury: pathogenic and opportunistic bacteria like The safety and efficacy of KD in human subjects has been extensively studied and confirmed in chronic neurological disorders . While sMK was involved in the literature search of the current state of science to be discussed in the review as well as consolidating ideas and writing up the greater majority of the review, created the tables and as well as the original artwork of the figures. AKS was MK’s primary mentor along the review writing process, guided the direction of MK’s research, provided frequent feedback, created the format and flow of the article, and edited multiple draft phases. NA provided meaningful edits in the draft phases of the review and contributed some information into the review. HY directed the formatting and scope of the review and provided meaningful feedback in the writing process. SJ, JG, KD, and BH provided meaningful feedback during the editing phase. All authors contributed to the article and approved the submitted version.


Dear Editor,Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide . The impWars, human rights violations, natural disasters, and economic hardships hitting Africa, the Middle East, Latin America, and South-East Asia in addition to the current state of Ukraine have led to a forcible displacement of tens of millions of people, creating an unprecedented refugee crisis . Unlike In addition to their suffering in their homelands from violence, persecution, and poverty and their host countries from unhygienic living conditions and discrimination , refugeeAccess of refugees to healthcare is mostly restricted and the quality of healthcare they obtain is questionable . PreviouAlthough CVD and COVID-19 are significant challenges to healthcare systems worldwide, these challenges should not be used as an excuse for blocking refugees from obtaining healthcare. Further, the failure to address healthcare inequity among refugees can undoubtedly hinder international efforts to reduce the CVD burden and contain the COVID-19 pandemic .While the refugee crisis is getting worse, several reforms in the healthcare systems of the host countries are needed to include refugees in the national health insurance programs and provide them with health counseling using their languages. Since refugees are at high risk of developing CVD and getting infected with COVID-19, they should be prioritized in CVD risk-prevention programs and COVID-19 vaccination plans. Besides, multi-centric prospective studies targeting refugees are needed to understand the emerging health disorders among this group. Further, providing appropriate preventive and curative health services to refugees is essential to reduce the heavy burden of CVD and COVID-19. These services should be accessible, equitable, and affordable, and refugees should actively participate in deciding the best health services that take into account social and cultural factors.