Abstract
Cloud computing environments provide users with Internet-based services and one of their main challenges is security issues. Hence, using Intrusion Detection Systems (IDSs) as a defensive strategy in such environments is essential. Multiple parameters are used to evaluate the IDSs, the most important aspect of which is the feature selection method used for classifying the malicious and legitimate activities. We have organized this research to determine an effective feature selection method to increase the accuracy of the classifiers in detecting intrusion. A Hybrid Ant-Bee Colony Optimization (HABCO) method is proposed to convert the feature selection problem into an optimization problem. We examined the accuracy of HABCO with BHSVM, IDSML, DLIDS, HCRNNIDS, SVMTHIDS, ANNIDS, and GAPSAIDS. It is shown that HABCO has a higher accuracy compared with the mentioned methods.

Abstract
Cloud Internet of Things (CIoT) environments, as the essential basis for computing services, have been subject to abuses and cyber threats. The adversaries constantly search for vulnerable areas in such computing environments to impose their damages and create complex challenges. Hence, using intrusion detection and prevention systems (IDPSs) is almost mandatory for securing CIoT environments. However, the existing IDPSs in this area suffer from some limitations, such as incapability of detecting unknown attacks and being vulnerable to the single point of failure. In this paper, we propose a novel distributed multi-agent IDPS (DMAIDPS) that overcomes these limitations. The learning agents in DMAIDPS perform a six-step detection process to classify the network behavior as normal or under attack. We have tested the proposed DMAIDPS with the KDD Cup 99 and NSL-KDD datasets. The experimental results have been compared with other methods in the field based on Recall, Accuracy, and F-Score metrics. The proposed system has improved the Recall, Accuracy, and F-Scores metrics by an average of 16.81%, 16.05%, and 18.12%, respectively.

Abstract
The recent proliferation of sensors and actuators, which is related to the Internet of Things (IoT), provide smart living to the general public in many data-critical areas, from homes and healthcare to power grids and transport [...]

Abstract
In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively increase the network capacity due to cell splitting and densification. A Base Station (BS) is a fixed transceiver that is the main communication point for one or more wireless mobile client devices. As UDNs are densely deployed, the number of BSs and communication links is dense, raising concerns about resource management with regard to energy efficiency, since BSs consume much of the total cost of energy in a cellular network. It is expected that 6G next-generation mobile networks will include technologies such as artificial intelligence as a service and focus on energy efficiency. Using machine learning it is possible to optimize energy consumption with cognitive management of dormant, inactive and active states of network elements. Reinforcement learning enables policies that allow sleep mode techniques to gradually deactivate or activate components of BSs and decrease BS energy consumption. In this work, a sleep mode management based on State Action Reward State Action (SARSA) is proposed, which allows the use of specific metrics to find the best tradeoff between energy reduction and Quality of Service (QoS) constraints. The results of the simulations show that, depending on the target of the 5G use case, in low traffic load scenarios and when a reduction in energy consumption is preferred over QoS, it is possible to achieve energy savings up to 80% with 50 ms latency, 75% with 20 ms and 10 ms latencies and 20% with 1 ms latency. If the QoS is preferred, then the energy savings reach a maximum of 5% with minimal impact in terms of latency.

Abstract
Task scheduling is a significant challenge in the cloud environment as it affects the network's performance regarding the workload of the cloud machines. It also directly impacts the consumed energy, therefore the profit of the cloud provider. This paper proposed an algorithm that prioritizes the tasks regarding their execution deadline. We also categorize the physical machines considering their configuration status. Henceforth, the proposed method assigns the jobs to the physical machines with the same priority class close to the user. Furthermore, we reduce the consumed energy of the machines processing the low-priority tasks using the DVFS method. The proposed method migrates the jobs to maintain the workload balance, or if the machines' class changed according to their scores. We have evaluated and validated the proposed method in the CloudSim library. The simulation results demonstrate that the proposed method optimized energy consumption by 12% and power consumption by 20%.

Abstract
Monitoring security and quality of service is essential, due to the rapid growth of the number of nodes in wireless networks. In healthcare/industrial environments, especially in wireless body area networks (WBANs), this is even more important. Because the delays and errors can directly affect patients'/scientists' health. To increase the Monitoring Quality of Services (MQoS) in WBANs, a secure medium access control (MAC) protocol needs to be developed to provide optimal services. This article provides a comprehensive review of MAC protocols in WBANs with a technical security analysis approach. Time-based, contention-based, and hybrid protocols are compared in this article, regarding MQoS and their security vulnerabilities. We have considered delay, packet loss, and energy consumption as performance evaluation criteria in WBANs, which may be degraded under a cyberattack. This work shows that there is a research gap in the literature, which is the failure of covering security and privacy issues in the MAC layer protocols.