An approach for detecting and distinguishing errors versus attacks in sensor networks

Distributed sensor networks are highly prone to accidental errors and malicious activities, owing to their limited resources and tight interaction with the environment. Yet only a few studies have analyzed and coped with the effects of corrupted sensor data. This paper contributes with the proposal of an on-the-fly statistical technique that can detect and distinguish faulty data from malicious data in a distributed sensor network. Detecting faults and attacks is essential to ensure the correct semantic of the network, while distinguishing faults from attacks is necessary to initiate a correct recovery action. The approach uses Hidden Markov Models (HMMs) to capture the error/attack-free dynamics of the environment and the dynamics of error/attack data. It then performs a structural analysis of these HMMs to determine the type of error/attack affecting sensor observations. The methodology is demonstrated with real data traces collected over one month of observation from motes deployed on the Great Duck Island.