The use of Internet of Things (IoT) technology entails incorporating numerous devices into heterogeneous networks, which is done in order to provide intelligence as well as automation in services. Yet, increased connectivity also poses various issues relating to the cyber-security of these networks, and resource-constrained nature of IoT devices hampers the effectiveness of conventional cyber-security techniques due to evolving threats. Intrusion Detection Systems (IDSs) has become an integral part of IoT network security. The purpose of this research is to analyze Machine Learning (ML)-based approaches to detect cyber-attacks in the environment of IoT. The review includes the types of learning approaches that were used in studies on existing IDSs, classifiers, databases, the types of attacks and anomalies detected, techniques to select features, as well as methods for evaluation of IDS. Thus, the analysis demonstrates that using machine learning enables to detect a variety of cyber-attacks and anomalies. Still, there are some barriers, such as imbalanced and small-size database, feature redundancy, computational constraints, false positives, and identification of new attacks. Thus, the review highlights the benefits and draw backs of currently existing IDS based on machine learning and proposes possible areas for future studies.
Mustafa Mohammed Jasim, Firas Mohammed Adress, A. Fadhil· European Multidisciplinary J...· 0 citations
The development of 6G wireless networks will meet a multitude of communication requirements including ultra-high data rates, extensive connectivity, low latency and intelligent network management. For these goals, the use of advanced technologies to improve spectrum utilization, computing efficiency and network scalability will be required. The current review covers three technologies that are expected to be prevalent in the future of 6G networks: intelligent multiple access, edge intelligence and advanced antenna systems. In this paper, we cover advancements in intelligent multiple access technologies to enhance spectrum efficiency and user connectivity, and the role played by edge intelligence in developing distributed artificial intelligence, real-time decision making, and latency-aware resource management. Moreover, we review advanced antenna technologies - massive Multiple Input Multiple Output (MIMO), cell-free architectures and reconfigurable intelligent surfaces - and discuss their potential to enhance coverage, capacity and energy efficiency. In addition, we also compare recent work and discuss some of the research gaps in the field and the future research directions in smart and sustainable 6G communication technologies. The findings illustrated that the integration of intelligent multiple access technologies, edge intelligence and advanced antenna technologies would be critical for reliable and scalable wireless networks that would be able to support next generation applications such as extended reality, autonomous systems and large-scale IoT systems.
Mustafa Mohammed Jasim, Firas Mohammed Adress, A. Fadhil· Central Asian Journal of The...· 0 citations
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