Multi-dimensional Resilient Statistical En-Route Filtering in Wireless Sensor Networks

Abstract

En-route filtering is an effective way to defeat false data injection attacks. Different from traditional one-dimensional en-route filtering mechanisms, we propose a multi-dimensional resilient statistical en-route filtering mechanism (MDSEF). In MDSEF, the overall key pool is divided into multiple sets and each set is composed then of a number of groups where each group is composed of a number of keys. Each node could join one group in each set and obtain some keys from these groups. Since each node obtains keys from multiple groups, the covering performance and filtering effectiveness of the mechanism can be improved simultaneously. An axis-rotation method is proposed to associate the terrain with multiple sets and implement location-based key derivation. Moreover, a stepwise refinement distributed group joining method is proposed for nodes' group selection. Analysis and simulation results show that MDSEF could significantly improve the covering performance and filtering effectiveness without losing the resiliency against node compromise.