A Group Formation Game for Local Anomaly Detection

This paper studies strategic group formation for local anomaly detection with potential applications to Cognitive Radio Networks (CRN) and the Internet-of-Things (IoT). The problem comprises multiple local anomaly detection tasks which use machine learning (ML) models and partial data. We consider a two-layer network structure with anomaly detectors in the lower layer acting as local anomaly detectors and central nodes at the upper layer as data aggregators, which train the ML models used by local anomaly detectors. The problem is addressed using a strategic (non-cooperative) game formulation, where all central nodes and detectors are players. The players interactively learn one or multiple optimal machine learning models for their dynamically identified local anomaly detection problems. The game is next formulated as a successive optimization problem and solved using the player's best responses to compute a Nash equilibrium. Under mild conditions, we prove that this group formation game is also an exact potential game. Experimental results are consistent with theoretical ones and show fast convergence to the solution.