Mitigating black hole attacks in wireless sensor networks using node-resident expert systems

Abstract

Wireless sensor networks consist of autonomous, self-organizing, low-power nodes which collaboratively measure data in an environment and cooperate to route this data to its intended destination. Black hole attacks are potentially devastating attacks on wireless sensor networks in which a malicious node uses spurious route updates to attract network traffic that it then drops. We propose a robust and flexible attack detection scheme that uses a watchdog mechanism and lightweight expert system on each node to detect anomalies in the behaviour of neighbouring nodes. Using this scheme, even if malicious nodes are inserted into the network, good nodes will be able to identify them based on their behaviour as inferred from their network traffic. We examine the resource-preserving mechanisms of our system using simulations and demonstrate that we can allow groups of nodes to collectively evaluate network traffic and identify attacks while respecting the limited hardware resources (processing, memory and storage) that are typically available on wireless sensor network nodes.