A Study on AI-Driven Multi-Layered Defense in 6G Ecosystems - Heena T Shaikh; Kazi Kutubuddin Sayyad Liyakat (International Journal of Radio Frequency Innovations, 2026)
The 6G networks bring about new degrees of possible functions related to connectivity, latency, data throughput, and integration with artificial intelligence (AI). This enables advances within healthcare, autonomous systems, and smart cities. The positive impact of rapid advancements must also be balanced with heightened risks due to the sheer volume of gaps that can be exploited, and the complex nature of the alignments and breaches. This results in the breaches being that much more complex to identify. Traditional security mechanisms no longer apply to the highly heterogeneous and dynamic environments present within 6G. This paper focuses on how the application of AI can significantly improve anomaly detection and breach identification in 6G applications. We illustrate an AI-driven, multi-layered framework that combines deep learning, federated learning, and reinforcement learning to behavioral patterns and network risk. AI can monitor complex intrusions and actively mitigate risks. The models can be trained to recognize patterns that deviate in response to events that indicate zero-day attacks, insider threats, and adversarial threats by analyzing large amounts of distributed data. In time, AI processes and models will explain, and improve the gap in latency to address problems of trust and operational feasibility of mission-critical applications. The results demonstrate accurate detection, minimization of false positives, and to demonstrate the transformative potential in securing the next generation of wireless ecosystems within these simulated 6G environments.
Keywords: 6G; Breach Detection; Cloud; Internet of Things; Zero-Day Attack; anomaly detection; artificial intelligence