In the rapidly advancing field of wireless communication, Unmanned Aerial Vehicles (UAVs) have become indispensable due to their extensive coverage capabilities and ability to access remote locations. Whether deployed as mobile base stations (BSs) or relays, UAVs significantly enhance network throughput and reliability. Alongside UAVs, Intelligent Reflecting Surfaces (IRS) have emerged as a cost-effective solution for improving communication quality through passive modulation arrays. Despite these advancements, the potential misuse of UAVs poses serious security risks, particularly in the form of communication eavesdropping. To address these challenges, this paper introduces a novel communication framework that integrates a UAV equipped with an adaptive IRS. The primary aim is to boost communication secrecy between BSs and multiple users, even in the presence of several UAV eavesdroppers. This objective is formulated as an optimization problem focused on maximizing the secrecy rate while considering UAV mobility constraints. To solve this non-convex problem, we propose a hybrid strategy that combines Genetic Algorithms and Gradient Descent techniques. This innovative approach efficiently determines suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs, thereby enhancing the security of the communication network. This method not only addresses the complexity of the optimization but also provides a practical pathway to secure communications in environments with high eavesdropping risks.

The identification of malicious users within a large set of participants poses a significant challenge in the domains of cybersecurity, data integrity, user management, and particularly within federated learning (FL) environments. FL, a distributed machine learning approach, necessitates rigorous mechanisms for safeguarding data integrity, model accuracy by effectively managing and identifying malicious participants. Traditional methods require the sequential removal and evaluation of users to determine their impact on the system’s overall error rate or loss function, fall short in terms of efficiency and scalability, especially in FL contexts where data is distributed across multiple clients. To address these limitations, we propose the Refine and Identify Algorithm, a two-phased approach that efficiently narrows the search space for identifying malicious users by initially evaluating users in groups rather than individually and iteratively focusing on those groups with the highest potential for containing malicious users. A rigorous mathematical framework, including a proof of convergence and a detailed analysis of iteration necessities, underpins the algorithm’s efficacy. The convergence proof and analysis of iteration requirements provide a solid mathematical foundation for the proposed method’s effectiveness, paving the way for further optimization and application-specific tuning. Simulation results depict the efficiency of the proposed technique and show a significant reduction in computational resources and time required for identifying malicious users.

In the dynamic realm of wireless communication, Unmanned Aerial Vehicles (UAVs) have gained increasing prominence due to their exceptional capabilities, which include expensive coverage of large areas and access to challenging and hazardous locations. When employed as mobile base stations or relays, UAVs have shown remarkable enhancements in system throughput and reliability. In addition, Intelligent Reflecting Surfaces (IRS) present a very low cost solution that efficiently enhances wireless communication quality using passive modulation arrays. Nevertheless, the use of UAVs for malicious intents can also introduce heightened security challenges such as communication eavesdropping. In response to these challenges, we present in this paper, a communication framework that incorporates a UAV equipped with an adaptive IRS aiming to enhance the communication secrecy between the Base Station (BS) and Bob in the presence of several UAV eavesdroppers. We formulate the objective as an optimization problem that aims to maximize the secrecy rate while considering the mobility constraints. To address this complex non-convex problem, our innovative solution harnesses a hybrid approach that combines Genetic Algorithms and Gradient Descent techniques, resulting in an efficient computation of suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs towards a more secure communication.

The Wireless Sensor Networks (WSN) are designed to remotely monitor and control specific physical or environmental conditions. However, due to the open nature of WSN, many threats and attacks may arise by malicious users such as jamming attacks. Several techniques have been developed for detecting such attack. However, the majority of these solutions try to decrease the impact of signal-jamming by increasing transmission power or using complicated coordinating schemes, which might be challenging in WSNs, where the sensor devices are limited in their energy and communication capabilities. In this paper, we present a new model for securing WSNs against jamming attacks based on the Colonel Blotto game where the equilibrium defines the minimization of the worst-case attack effect on the sensors communications. Then, by investigating the Nash Equilibrium (NE) of the game and for all the potential attackers, the system computes the optimal power allocation strategy to protect the network against the malicious nodes. The simulation results show that the proposed model can secure the channel communication for WSN by 55% compared to the other technique while using the same network resources.

A new generation of advanced information technologies are used nowadays by healthcare systems to provide access to affordable and high-quality healthcare services. However, such services, generally require a large amount of data that needs to be stored, shared and secured efficiently. Therefore, a secure distributed data storing and sharing mechanism is proposed in this paper for healthcare systems. By using a specifically designed data splitting and encryption approach, the proposed sharing mechanism ensures that any user in the network can recuperate the data only if it gets the approval of a prefixed number of trusted nodes. This ensure that although the data is distributed among different network nodes, it becomes useless without the collection of the necessary data parts and approvals. In order to enhance the data sharing robustness to failures while minimizing the transmission delays, and satisfying the network constraints, an iterative algorithm is proposed to optimize the selection of the nodes that should participate in the data storage process. The simulation results confirm the efficiency of the proposed approaches to efficiently and securely store and share the data with the other legitimate nodes.

Cloud computing is a paradigm that provides multiple services over the internet with high flexibility in a cost-effective way. However, the growth of cloud-based services comes with major security issues. Recently, machine learning techniques are gaining much interest in security applications as they exhibit fast processing capabilities with real-time predictions. One major challenge in the implementation of these techniques is the available training data for each new potential attack category. In this paper, we propose a new model for secure network based on machine learning algorithms. The proposed model ensures better learning of minority classes using Generative Adversarial Network (GAN) architecture. In particular, the new model optimizes the GAN parameter including the number of inner learning steps for the discriminator to balance the training datasets. Then, the optimized GAN generates highly informative “like real” instances to be appended to the original data which improve the detection of the classes with relatively small training data. Our experimental results show that the proposed approach enhances the overall classification performance and detection accuracy even for the rarely detectable classes for both UNSW and NSL-KDD datasets. The simulation results show also that the proposed model could detect better the network attacks compared to the state-of-art techniques.

Cloud computing is attracting a lot of attention in the past few years. Although, even with its wide acceptance, cloud security is still one of the most essential concerns of cloud computing. Many systems have been proposed to protect the cloud from attacks using attack signatures. Most of them may seem effective and efficient; however, there are many drawbacks such as the attack detection performance and the system maintenance. Recently, learning-based methods for security applications have been proposed for cloud anomaly detection especially with the advents of machine learning techniques. However, most researchers do not consider the attack classification which is an important parameter for proposing an appropriate countermeasure for each attack type. In this paper, we propose a new firewall model called Secure Packet Classifier (SPC) for cloud anomalies detection and classification. The proposed model is constructed based on collaborative filtering using two machine learning algorithms to gain the advantages of both learning schemes. This strategy increases the learning performance and the system's accuracy. To generate our results, a publicly available dataset is used for training and testing the performance of the proposed SPC. Our results show that the accuracy of the SPC model increases the detection accuracy by 20% compared to the existing machine learning algorithms while keeping a high attack detection rate.

Smart health systems improve the existing health services by integrating information and technology into health and medical practices. However, smart healthcare systems are facing major challenges including limited network resources, energy allocation, and latency. In this paper, we leverage the dense heterogeneous network (HetNet) architecture over 5G network to enhance network capacity and provide seamless connectivity for smart health systems. The network selection and energy allocation in HetNets are important factors in this regard due to their significant impact on system performance. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, we present a novel DRL model for energy-efficient network selection in heterogeneous health systems. The proposed model selects the set of networks to be used for data transmission with adaptive compression at the edge with an optimal energy allocation policy for all the network participants. Our experimental results show that the proposed DRL model has a good performance compared to the existing state of art techniques while meeting different users' demands in highly dynamic environments.

In this paper, we conduct a biological data collection and analysis study for patients undergoing routine planned intravenous antibiotic treatment. The acquired data (i.e., Electroencephalogram (EEG), temperature and blood pressure) are processed using different machine learning and deep learning models to learn the dynamic properties of brain electrical activity from this group of patients. Thus, the primary objective of our study is the safe collection of EEG data from patients receiving antibiotic therapy, in addition to analyzing the acquired data for patterns that might indicate risk of seizure. We propose two machine learning models to analyze the acquired data from these patients split into three classes: data collected before, during, and after receiving the medication. Our results show the effectiveness of our models in analyzing the acquired data, which would not possible by imitative human analysis.

Network security is an critical subject in any distributed network. Recently, machine learning has proven their efficiency for intrusion detection. By using a comprehensive dataset with multiple attack types, a well-trained model can be created to improve the anomaly detection performance. However, high dimensional data sets are a significant challenge for machine learning. In fact, learning algorithms considering all features in the input data, may cause over-fitting to irrelevant aspects of the data and increase the computational time caused by the process of similar features that provide redundant information, which is a critical problem especially for users with constrained resources. In this paper, we propose a new and efficient feature selection technique for intrusion detection in modern networks called Iterative Per Group Feature Selection (IPGFS). IPGFS reduces the number of features in the input data and selects the best features using the performance accuracy of the classifier. The features are sorted and selected according to their accuracy score. Both the UNSW and NSLKDD datasets are used in this paper to validate the proposed model and verify its efficiency in detecting intrusions. The simulation results show that the proposed model can reduce the number of features for the two dataset while successfully detecting intrusions with better accuracy compared to state-of-the-art techniques. Index Cloud security, feature selection, accuracy, machine learning techniques.

Smart health systems improve the efficiency of healthcare infrastructures and biomedical systems by integrating information and technology into health and medical practices. However, reliability, scalabilty and latency are among the many challenges hindering the realization of next-generation healthcare. In fact, with the exponential increases in the volume of patient data being produced and processed, many healthcare system' are being overwhelmed with the deluge of data they are facing. Many systems have been proposed to improve the system latency and scalability. However, there are concerns related to some of theses systems regarding the increasing levels of required human interaction which impact their efficiency. Recently, machine learning techniques are gaining a lot of interest in health applications as they exhibit fast processing with realtime predictions. In this paper, we propose a new healthcare system to reduce the waiting time in emergency department and improve the network scalability in any distributed system. The proposed model integrates the power of edge computing with machine learning techniques for providing a good quality of healthcare services. The machine learning algorithm is used to generate a classifier that can predict with high levels of accuracy the likelyhood of a patient to have a heart attack using his physiological signals ECG. The proposed system stores the patient data in a centralized database and generates a unique index using a new data-dependent Indexing algorithm that transforms the patient data into unique code to be sent for any medical data exchange. Multiple machine learning algorithms are studied and the best algorithm will be selected based on efficient performance result for the prediction of heart attack problem. simulation results show that the proposed model can effectively detect the abnormal heart beats with 91% using SVM algorithm. We show also that the proposed system outperforms conventional indexing algorithm systems in terms of collisions rate.

Recently, machine learning techniques are gaining a lot of interest in security applications as they exhibit fast processing with real-time predictions. One of the significant challenges in the implementation of these techniques is the collection of a large amount of training data for each new potential attack category, which is most of the time, unfeasible. However, learning from datasets that contain a small training data of the minority class usually produces a biased classifiers that have a higher predictive accuracy for majority class(es), but poorer predictive accuracy over the minority class. In this paper, we propose a new designed attacks weighting model to alleviate the problem of imbalanced data and enhance the accuracy of minority classes detection. In the proposed system, we combine a supervised machine learning algorithm with the node1past information. The machine learning algorithm is used to generate a classifier that differentiates between the investigated attacks. Then, the system stores these decisions in a database and exploits them for the weighted attacks classification model. Thus, for each attack class, the weight that maximizes the detection of the minority classes will be computed and the final combined decision is generated. In this work, we use the UNSW dataset to train the supervised machine learning model. The simulation results show that the proposed model can effectively detect intrusion attacks and provide better accuracy, detection rates and lower false alarm rates compared to state-of-the art techniques.1In this document we will use the words “node” to represent computing, storage, physical, and virtual machines.

The growth of cloud-based services is mainly supported by the core networking infrastructures of large-scale data centers, while the scalability of these services is influenced by the performance and dependability characteristics of data centers. Hence, the data center network must be agile and reconfigurable in order to respond quickly to the ever-changing application demands and service requirements. The network must also be able to interconnect the big number of nodes, and provide an efficient and fault-tolerant routing service to upper-layer applications. In response to these challenges, the research community began exploring novel interconnect topologies, namely: Flecube, DCell, Ficonn, HyperFlaNet and BCube. However, these topologies either scale too fast (grows exponentially in size), or too slow, and therefore suffer from performance bottlenecks. In this paper, we propose a novel data center topology called LaScaDa (Layered Scalable Data Center) as a new solution for building scalable and cost-effective data center networking infrastructures. The proposed topology organizes nodes in clusters of similar structure, then interconnect these clusters in a well-crafted pattern and system of coordinates for nodes to reduce the number of redundant connections between clusters, while maximizing connectivity. LaScaDa forwards packets between nodes using a new hierarchical row-based routing algorithm. The algorithm constructs the route to the source based on the modular difference between the source and destination coordinates. Furthermore, the proposed topology interconnects a large number of nodes using a small node degree. This strategy increases the number of directly connected clusters and avoids redundant connections. As a result, we get a good quality of nodes in terms of average path length (APL), bisection bandwidth, and aggregated bottleneck throughput. Experimental results show that LaScaDa has better performance than DCell, BCube, and HyperBcube in terms of scalability, while providing a good quality of service.

Cloud computing has been presented as one of the most efficient techniques for hosting and delivering services over the internet. However, even with its wide areas of application, cloud security is still a major concern of cloud computing. In order to protect the communication in such environment, many secure systems have been proposed and most of them are based on attack signatures. These systems are often not very efficient for detecting all the types of attacks. Recently, machine learning technique has been proposed. This means that if the training set does not include enough examples in a particular class, the decision may not be accurate. In this paper, we propose a new firewall scheme named Enhanced Intrusion Detection and Classification (EIDC) system for secure cloud computing environment. EIDC detects and classifies the received traffic packets using a new combination technique called most frequent decision where the nodes'11In this document we will use the words “node” and “user” interchangeably.past decisions are combined with the current decision of the machine learning algorithm to estimate the final attack category classification. This strategy increases the learning performance and the system accuracy. To generate our results, a public available dataset UNSW-NB-15 is used. Our results show that EICD improves the anomalies detection by 24% compared to complex tree.

Many proposed data center architectures are constructed with a huge number of network devices in order to support the increasing cloud based services. These devices are used to achieve the highest performance in case of full utilization of the network. However, the peak capacity of the network is rarely reached. As a result, many devices are set into idle state which increases the network energy consumption and lead to a non-proportionality between the consumed energy and the network load. In this paper, we present a new approach that reduces the data center energy consumption with a reduced trade off on network performance. By exploiting the correlation in time of internode communication and some topological features, the proposed approach uses the outdated traffic matrix to control the set of active communication links and ports in the network (switches ports and nodes ports). The ports activation management process is done using a proposed algorithm that guarantees network connectivity. Extensive simulations have been conducted to validate the performance of the proposed scheme in terms of average path length and energy consumption.

Data centers have an important role in supporting cloud computing services (i.e. checking social media, sending emails, video conferencing,..). Hence, data centers topologies design became more important and must be able to respond to ever changing service requirements and application demands. An ultimate challenge in this research is the design of data center network that interconnects the massive number of servers, and provides efficient and fault-tolerant routing algorithm. Several topologies such as DCell, FlatNet and ScalNet have been proposed. However, these topologies generally seek to improve the scalability without taking into consideration the energy usage neither the network nfrastructure cost which is critical parameters in data centers. Motivated by these challenges, we propose a new network topology for data center, called AdyNet. It is an adaptive, dynamic, cost effective and highly performing topology. While reducing largely the infrastructure cost and the energy consumption, AdyNet outperforms FlatNet and ScalNet in terms of Average Path Length.

Todays data centers may contain tens of thousands of computers with progressively more specialized and expensive equipments. Thus, the research community has proposed various interconnect topologies e.g FatTree, Dcell and Bcube. However, these architectures are too complex and very expensive to construct and they suffer from high average path length (APL) and latency. Motivated by these challenges, we propose a new data center architecture called VacoNet that combines the advantages of existing architectures while avoiding their limitations. VacoNet is a reliable, high-performance, and cost effective data center topology that can improve the network performance in terms of average path length and network latency. In addition, VacoNet can reach even 50% in infrastructure cost reduction and the power consumption will be decreased with more than 50000 watt compared to all the previous architectures. Both theoretical analysis and simulation experiments are conducted to evaluate the overall performance of the proposed architecture.

Cooperative spectrum sensing (CSS) has been proposed as a solution for radio spectrum resources scarcity problem. Clustering technique is introduced to increase the performance of CSS and overcome the shadowing and fading effects, also to reduce the control channel overhead for big number of cooperative users. However, Clustering is facing two major issues which are the data falsification caused by the malicious users and the increasing delay and energy consumption especially when the cluster heads is far away from the fusion center. In this paper, we propose a cluster and forward based on the trust CSS. By dividing all the secondary users into clusters and using of the trust threshold for selecting the most trusted CHs that can send their sensing decisions to the fusion center which can save more energy consumption and delay transmission, and improve the spectrum sensing performance.