An Approach To Enhance IoT Security In 6G Networks Through Explainable AI

6G networks will dramatically expand IoT security risks: The paper identifies that next-generation 6G technology—expected to be finalized by 2030—introduces multiple new vulnerabilities through advanced features like open RAN, terahertz communication, and AI systems, significantly expanding the attack surface that security teams must protect.

AI itself can become a security vulnerability: Beyond using AI for defense, the research highlights that AI exploitation represents an emerging threat in 6G-IoT systems, meaning the intelligent systems designed to protect networks can themselves be compromised or manipulated by attackers.

Explainability is critical for trustworthy AI security: The paper demonstrates that using explainable AI methods (SHAP and LIME) to show why security systems flag threats as dangerous makes those systems more transparent and reliable, helping security teams understand and verify that AI-based defenses are working correctly rather than making arbitrary decisions.

Current security frameworks have significant gaps: The research emphasizes that existing security measures are not adequately aligned with 6G technological advances, meaning organizations cannot yet rely on established protocols to protect IoT devices in next-generation networks.

Balanced data representation improves fairness in AI security: By ensuring that training data fairly represents different types of attacks rather than over-representing common ones, the researchers improved both model accuracy and the fairness of their security system—an important consideration for responsible AI deployment.