LFDD: Local False Data Detection for In-Network Aggregation in Wireless Sensor Networks

To minimize the damage of false data injection attack, detecting the false injected data for the in-network aggregation in sensor networks at an early stage becomes a necessary and challenging task. In this paper, we adopt Hierarchical Bayesian Space-Time (HBST) methodology to characterize the data model of in-network aggregation, and propose a divided difference filter (DDF) based scheme LFDD to detect false injected data. We evaluate the performance of the proposed scheme via the theoretical analysis, and conduct the experiments on the Telosb platform using TinyOS-2.1 to validate the theoretical results. Both the theoretical and experimental results indicate that LFDD is suitable to resist false data injection attack for aggregation in WSNs.