Enhanced Security in 6G Cognitive Networks: A Multi-Objective Approach Against Advanced Threats

The increasing security challenges in 6G cognitive networks necessitate advanced protection mechanisms against sophisticated threats. This letter presents Multi-Objective Security Enhancement for Cognitive Networks (MOSE-CN), introducing a novelsecurity-aware federated meta-learningframework that fundamentally advances beyond existing approaches. Our core innovation lies in theAdaptive Security Manifold(ASM) theory, which mathematically unifies multi-threat modeling with distributed learning convergence guarantees. Unlike conventional methods that treat security and learning as separate concerns, ASM establishes that optimal security policies exist on a learnable manifold whose geometry adapts to threat dynamics. We provide rigorous theoretical analysis including complete convergence proofs and computational complexity bounds. Validation through both extensive simulations using 3GPP TR 38.901 models and preliminary USRP-based testbed experiments demonstrates 28.3±2.1% improvement in multi-eavesdropper secrecy rate and 35.7±3.2% enhanced jamming resilience compared to both centralized and recent distributed approaches including blockchain-based and quantum-resistant schemes. Real-world deployment scenarios in urban 6G environments confirm practical applicability with O(N log N) complexity.