Statistical En-route Detection and Filtering of Injected False Data in Sensor Networks
In a large-scale sensor network individual sensors are subject to security compromises. A compromised node can inject bogus sensing reports into the network. If undetected, these bogus reports would be forwarded to the data collection point (i.e. the sink). Such attacks by compromised sensors can cause not only false alarms but also the depletion of the finite amount of energy in a battery powered network. In this paper, the authors present a Statistical Enroute Filtering (SEF) mechanism that can detect and drop such false reports. SEF requires that each sensing report be validated by multiple keyed Message Authentication Codes (MACs), each generated by a node that detects the same event.