Detecting stealthy attacks: Efficient monitoring of suspicious activities on computer networks. (October 2015)

• Record Type: Journal Article
• Authors: Kalutarage, Harsha K.; Shaikh, Siraj A.; Wickramasinghe, Indika P.; Zhou, Qin; James, Anne E.
• Is Part Of: Computers & electrical engineering. Volume 47(2015)
• Page Start: 327
• Page End: 344
• Publication Date: 2015-10
• Subjects: Stealthy attacks -- Bayesian fusion -- Network simulation -- Traffic sampling -- Anomaly detection; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.07.007
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: A scalable monitoring scheme for stealthy attacks on computer networks is presented. Bayesian fusion along with traffic sampling is used as a data reduction method. Stealthy activities can be detected using 10–20% size sampling rates. A tracing algorithm for anonymous stealthy activities to their sources is presented. The effect of network parameters on detection is investigated. 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.