Secure AI for 6G Networks: Addressing Side-Channel Attacks

Mohammed Saeb Nahi

Vol. 11, Issue 1, Jan-Dec 2025

Page Number: 1 - 11

Abstract:

6t generation networks, such as ultra-low latency, massive device connection, and high data throughput, along with formidable applications including autonomous systems, smart cities, and advanced healthcare, has arrived. Unfortunately, these advancements also bring about significant security issues, namely side channel attacks that use non-functional data, i.e., power consumption and timing information to leak out sensitive information. In this paper, deep learning optimization strategies for making 6G mobile devices more secure against such vulnerabilities are presented. The proposed framework integrates robust encryption techniques with AI powered secure key management mechanisms to mitigate threats of potential threats while bolstering robust and adaptive defenses in dynamic 6G environments. We observe via extensive experimental results that optimized deep learning models can accurately detect and counter side-channel exploits with a detection accuracy approaching 95%. In addition, AI driven encryption is able to achieve significantly better performance and resilience by achieving lower computational over head while having equally high security. It highlights the importance of artificial intelligence in solving growing cybersecurity challenges and paving the way for the secure deployment of next generation networks. The findings underline the need for AI driven methodologies in protecting the integrity and privacy of the data being deployed in 6G systems.

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