Interactive Wormhole Detection in Large Scale Wireless Network

Authors: Weichao Wang and Aidong Lu

This paper develops an approach, Interactive Visualization of Wormholes (IVoW), to monitor and detect such attacks in large scale wireless networks in real time. We characterize the topology features of a network under wormhole attacks through the node position changes and visualize the information at dynamically adjusted scales. We integrate an automatic detection algorithm with appropriate user interactions to handle complicated scenarios that include a large number of moving nodes and multiple wormhole attackers. Various visual forms have been adopted to assist the understanding and analysis of the reconstructed network topology and improve the detection accuracy. Extended simulation has demonstrated that the proposed approach can effectively locate the fake neighbor connections without introducing many false alarms. IVoW does not require the wireless nodes to be equipped with any special hardware, thus avoiding any additional cost. The proposed approach demonstrates that interactive visualization can be successfully
combined with network security mechanisms to greatly improve the intrusion detection capabilities.

UNCC Technical Report # CVC-UNCC-06-05