In an ever-evolving global landscape, concerns regarding network security continue to grow. Integrating information technologies into daily life has made safeguarding computer security imperative. The rise of internet connectivity and innovations like the Internet of Things (IoT) have introduced new challenges in breaching computer systems. Organizations are dedicating resources to research methods for enhancing cyber-attack discovery, opting for intelligent approaches to achieve the highest accuracy rates. The combination of IoT and ML is changing how services and applications work. In the classical ML approaches, data are collected and centrally processed. Nevertheless, this approach is challenging to implement in modern IoT networks because they deal with a significant amount of data, and privacy is often an issue. In contrast, federated learning (FL) has been reported as a possible approach to address such limitations. FL enables ML methods to perform collaborative training through model parameter sharing rather than client data. This study comprehensively reviews cutting-edge literature on enhancing computer network security with ML in the FL environment and IoT. This work further explores various methods and applications in intrusion detection (ID) mechanisms within computer networks through a contemporary and thorough examination.
Keywords
Machine Learning, Internet of Things, Detection System, Federated Learning, Intelligent Techniques, Network Security.
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Author(s) thanks to Dr. Wathiq Laftah Al-Yaseen for this research completion and support.
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Qusay Abdullah Abed
Computer Systems Department, Kerbala Technical Institute, Al-Furat Al-Awsat Technical University, Kerbala, Iraq.
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Cite this article
Qusay Abdullah Abed and Wathiq Laftah Al-Yaseen, “Intrusion Detection Systems for IoT Based on Machine Learning Under the Learning Environment”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505018.
