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 compr omised 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 we 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. As the report is forwarded, each node along the way verifies the correctness of the MACs probabilistically and drops those with invalid MACs at earliest points. The sink further filters out remaining false reports that escape the en-route filtering. SEF exploits the network scale to determine the truthfulness of each report through collective decision-making by multiples detecting nodes and collecti ve false-report-detection by multiple forwarding nodes. Our analysis and simulations show that, with an overhead of 14 bytes per report, SEF is able to drop 80 90% falsely injected reports by a compromised node within 10 forwarding hops.