5G Network Intrusion Detection Method Based on Robust Federated Optimization
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Abstract
High-speed, low-latency and massive connectivity have emerged as a result of the rapid development of 5G networks, but so have security threats. Current intrusion detection tools are poorly adapted to the distributed, heterogeneous, and dynamic 5G environment where a flood of real-time information is generated over a spectrum of devices at the edges and network layers. Federated learning has been suggested in response to these threats as a new paradigm to aid in the training of intrusion detection systems without having to aggregate the information. This review provides a detailed study of intrusion detection systems in 5G networks that are federated learning-based, and how the federated learning-based intrusion detection systems can address the challenges mentioned above. The paper will also entail the discussion of the basic ideas and principles of federated learning, the importance of robust federated optimization techniques to enhance the robustness of models, and architectural design of distributed intrusion detection systems. Moreover, accuracy, efficiency, and resilience to adversarial attacks are also used as indicators of performance, which emphasizes the potential and strength of federated learning when used in complex network conditions. The key challenges and the difficulties, including the heterogeneity, communication, and security, are also discussed and analyzed. Lastly, the new trends and possible directions of research, such as the combination of AI explanations, adaptive learning, and federated learning with other emerging technologies, such as edge computing and blockchain, are also presented. In general, the paper provides a detailed and extensive perspective on the design and development of scalable and privacy-conserving and smart intrusion detection mechanisms on next-generation 5G networks.
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