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Zero-Trust and Reinforcement Learning for Secure Federated Intelligence in 6G Edge Networks

Summary:

The introduction of 6G networks will bring unprecedented advancements in connectivity, intelligence, and automation. However, this integration of AI also exposes 6G networks to sophisticated cyber threats, including adversarial attacks, model poisoning, and AI-driven malware. Traditional security mechanisms are inadequate against these threats, necessitating proactive security solutions. In response, this paper presents a zero-trust security framework tailored for decentralised AI inference in 6G environments. It enforces continuous verification and identity-based access control by integrating Self-Sovereign Identity with Zero-Knowledge Proofs. Anonymity is ensured through zk-SNARK-based membership proofs, allowing edge nodes to authenticate without revealing their identities. Sybil resistance is achieved by registering cryptographic commitments derived from unique Verifiable Credentials within an on-chain Merkle tree, thereby ensuring one-time registration of each legitimate node. To further strengthen trust and resilience, a reinforcement learning-based trust mechanism is deployed at the aggregator level to evaluate participating devices, facilitating the detection and isolation of malicious clients

Author(s):

Gueltoum Bendiab    
University of frère Mentouri
Algeria

Meriem Guerar    
DIBRIS, University of Genoa
Italy

Houda Haiouni    
University of frère Mentouri
Algeria

Luca Verderame    
DIBRIS, University of Genoa
Italy