Research Article
Important Member Discovery of Attribution Trace Based on Relevant Circle (Short Paper)
@INPROCEEDINGS{10.1007/978-3-030-12981-1_16, author={Jian Xu and Xiaochun Yun and Yongzheng Zhang and Zhenyu Cheng}, title={Important Member Discovery of Attribution Trace Based on Relevant Circle (Short Paper)}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={2}, keywords={Importance rank Network attribution Relevance}, doi={10.1007/978-3-030-12981-1_16} }- Jian Xu
Xiaochun Yun
Yongzheng Zhang
Zhenyu Cheng
Year: 2019
Important Member Discovery of Attribution Trace Based on Relevant Circle (Short Paper)
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-12981-1_16
Abstract
Cyberspace attack is a persistent problem since the existing of internet. Among many attack defense measures, collecting information about the network attacker and his organization is a promising means to keep the cyberspace security. The exposing of attackers halts their further operation. To profile them, we combine these retrieved attack related information pieces to form a trace network. In this attributional trace network, distinguishing the importance of different trace information pieces will help in mining more unknown information pieces about the organizational community we care about. In this paper, we propose to adopt relevant circle to locate these more important vertices in the trace network. The algorithm first uses Depth-first search to traverse all vertices in the trace network. Then it discovers and refines relevant circles derived from this network tree, the rank score is calculated based on these relevant circles. Finally, we use the classical 911 covert network dataset to validate our approach.