Publikation
Defending Against Adversarial Attacks in 6G: Practical Mitigation Approach
Sogo Pierre Sanon; Akshay Sant; Hans Dieter Schotten
In: Proceedings of the Eighth International Balkan Conference on Communications and Networking. International Balkan Conference on Communications and Networking (BalkanCom-2025), June 17-20, Piraeus, Greece, IEEE Xplore, 2025.
Zusammenfassung
The integration of Artificial Intelligence (AI) into mobile networks has led to significant improvements in operational efficiency, resource optimization, and security monitoring. However, the increasing reliance on AI has also introduced vulnerabilities to Adversarial Machine Learning (AML) threats, which are expected to become even more critical with the advent of 6G networks. While numerous AML techniques have been identified, not all pose substantial risks to mobile communication systems. Implementing defenses against all possible attacks can be computationally expensive and may degrade system performance. This study critically examines adversarial threats in AI-driven mobile networks, and attacks are categorized based on their feasibility and real-world impact. A risk-based framework is presented to assist in efficiently prioritizing security investments. The most critical AML threats to mobile networks are identified, and targeted mitigation strategies that ensure a balance between security, computational efficiency, and system reliability are provided. The findings of this research serve as a guideline for AI security implementation in future networks, promoting a strategic approach to adversarial defense while maintaining high network performance.