Attack detectors for data aggregation in clustered sensor networks

Among many security threats to sensor networks, compromised sensing is particularly challenging due to the fact that it cannot be addressed by standard authentication approaches. We consider a clustered scenario for data aggregation in which an attacker injects a disturbance in sensor readings. Casting the problem in an estimation framework, we systematically apply the Generalized Likelihood Ratio approach to derive attack detectors. The analysis under different attacks reveals that detectors based on similarity of means across clusters are suboptimal, with Bartlett's test for homoscedasticity constituting a good candidate when lacking a priori knowledge of the variance of the underlying distribution.