  The conventional device authentication of wireless networks usually relies on
a security server and centralized process, leading to long latency and risk of
single-point of failure. While these challenges might be mitigated by
collaborative authentication schemes, their performance remains limited by the
rigidity of data collection and aggregated result. They also tend to ignore
attacker localization in the collaborative authentication process. To overcome
these challenges, a novel collaborative authentication scheme is proposed,
where multiple edge devices act as cooperative peers to assist the service
provider in distributively authenticating its users by estimating their
received signal strength indicator (RSSI) and mobility trajectory (TRA). More
explicitly, a distributed learning-based collaborative authentication algorithm
is conceived, where the cooperative peers update their authentication models
locally, thus the network congestion and response time remain low. Moreover, a
situation-aware secure group update algorithm is proposed for autonomously
refreshing the set of cooperative peers in the dynamic environment. We also
develop an algorithm for localizing a malicious user by the cooperative peers
once it is identified. The simulation results demonstrate that the proposed
scheme is eminently suitable for both indoor and outdoor communication
scenarios, and outperforms some existing benchmark schemes.
