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The conventional device authentication of wireless networks usually relies ona security server and centralized process, leading to long latency and risk ofsingle-point of failure. While these challenges might be mitigated bycollaborative authentication schemes, their performance remains limited by therigidity of data collection and aggregated result. They also tend to ignoreattacker localization in the collaborative authentication process. To overcomethese challenges, a novel collaborative authentication scheme is proposed,where multiple edge devices act as cooperative peers to assist the serviceprovider in distributively authenticating its users by estimating theirreceived signal strength indicator (RSSI) and mobility trajectory (TRA). Moreexplicitly, a distributed learning-based collaborative authentication algorithmis conceived, where the cooperative peers update their authentication modelslocally, thus the network congestion and response time remain low. Moreover, asituation-aware secure group update algorithm is proposed for autonomouslyrefreshing the set of cooperative peers in the dynamic environment. We alsodevelop an algorithm for localizing a malicious user by the cooperative peersonce it is identified. The simulation results demonstrate that the proposedscheme is eminently suitable for both indoor and outdoor communicationscenarios, and outperforms some existing benchmark schemes.

Collaborative Authentication for 6G Networks: An Edge Intelligence based Autonomous Approach