Sybil Attack Detection through Global Topology Pattern Visualization

We present a robust intrusion detection approach for wireless networks based on a new multi-matrix visualization method with a set of pattern generation, evaluation, organization, and interaction functions. Our approach concentrates on assisting users to analyze statistical network topology patterns that could expose significant attack features. Specifically, we investigate Sybil attacks that have severe impacts on the fundamental operations of wireless networks. We have analyzed the features of network topologies under various Sybil attacks and, consequently, designed several matrix reordering algorithms to generate statistical patterns. These topology patterns are automatically evaluated and classified through the measured structural similarities to the signature attack patterns. We have also designed a new time-series analysis method to identify attack durations with a time histogram generation and an automatic segmentation method. To handle complex Sybil attacks, we have integrated our pattern generation, evaluation, and organization methods to construct a prototype detection system, in which specialized interaction functions are provided to assist the analysis and comparison of network data. Simulation results show that this approach can effectively locate Sybil attacks under different combinations of network parameters. Our multi-matrix visualization method provides a flexible framework to handle the intricacies and implications from building a complex visual analytics system, which can be extended to defend against a wide range of attacks.