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Article type: Research Article
Authors: Xia, Jianhonga | Vangala, Sarmaa | Wu, Jianga | Gao, Lixina | Kwiat, Kevinb
Affiliations: [a] Department of Electrical and Computer Engineering, University of Massachusetts at Amherst, Amherst, MA 01003, USA. E-mail: [email protected], [email protected], [email protected], [email protected] | [b] Air Force Research Lab, Information Directorate, 525 Brooks Road, Rome, NY 13441, USA. E-mail: [email protected]
Abstract: In recent years, the threats and damages caused by active worms have become more and more serious. In order to reduce the loss caused by fast-spreading active worms, an effective detection mechanism to quickly detect worms is desired. In this paper, we first explore various scan strategies used by worms on finding vulnerable hosts. We show that targeted worms spread much faster than random scan worms. We then present a generic worm detection architecture to monitor malicious worm activities. We propose and evaluate our detection mechanism called Victim Number Based Algorithm. We show that our detection algorithm is effective and able to detect worm events before 2% of vulnerable hosts are infected for most scenarios. Furthermore, in order to reduce false alarms, we propose an integrated approach using multiple parameters as indicators to detect worm events. The results suggest that our integrated approach can differentiate worm attacks from DDoS attacks and benign scans.
Keywords: Worm modeling, worm scan techniques, worm detection algorithm, network security
DOI: 10.3233/JCS-2006-14403
Journal: Journal of Computer Security, vol. 14, no. 4, pp. 359-387, 2006
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