Detecting stealthy attacks: efficient monitoring of suspicious activities on computer networks.

Kalutarage, Harsha K.; Shaikh, Siraj A.; Wickramasinghe, Indika P.; Zhou, Qin; James, Anne E.

Abstract

Stealthy attackers move patiently through computer networks – taking days, weeks or months to accomplish their objectives in order to avoid detection. As networks scale up in size and speed, monitoring for such attack attempts is increasingly a challenge. This paper presents an efficient monitoring technique for stealthy attacks. It investigates the feasibility of proposed method under number of different test cases and examines how design of the network affects the detection. A methodological way for tracing anonymous stealthy activities to their approximate sources is also presented. The Bayesian fusion along with traffic sampling is employed as a data reduction method. The proposed method has the ability to monitor stealthy activities using 10–20% size sampling rates without degrading the quality of detection.

Citation

KALUTARAGE, H.K., SHAIKH, S.A., WICKRAMASINGHE, I.P., ZHOU, Q. and JAMES, A.E. 2015. Detecting stealthy attacks: efficient monitoring of suspicious activities on computer networks. Computers and electrical engineering [online], 47, pages 327-344. Available from: https://doi.org/10.1016/j.compeleceng.2015.07.007

• Journal Article Type: Article
• Acceptance Date: Jul 8, 2015
• Online Publication Date: Jul 18, 2015
• Publication Date: Oct 31, 2015
• Deposit Date: Feb 3, 2020
• Publicly Available Date: Feb 3, 2020
• Journal: Computers and electrical engineering
• Print ISSN: 0045-7906
• Electronic ISSN: 1879-0755
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 47
• Pages: 327-344
• DOI: https://doi.org/10.1016/j.compeleceng.2015.07.007
• Keywords: Stealthy attacks; Bayesian fusion; Network simulation; Traffic sampling; Anomaly detection
• Public URL: https://rgu-repository.worktribe.com/output/816388