標題: Behavior-based botnet detection in parallel
作者: Wang, Kuochen
Huang, Chun-Ying
Tsai, Li-Yang
Lin, Ying-Dar
資訊工程學系
Department of Computer Science
關鍵字: anomaly detection;behavior-based;botnet detection;cloud computing;fuzzy pattern recognition;parallel process
公開日期: 1-Nov-2014
摘要: Botnet has become one major Internet security issue in recent years. Although signature-based solutions are accurate, it is not possible to detect bot variants in real-time. In this paper, we propose behavior-based botnet detection in parallel (BBDP). BBDP adopts a fuzzy pattern recognition approach to detect bots. It detects a bot based on anomaly behavior in domain name service (DNS) queries and transmission control protocol (TCP) requests. With the design objectives of being efficient and accurate, a bot is detected using the proposed five-stage process, including: (i) traffic reduction, which shrinks an input trace by deleting unnecessary packets; (ii) feature extraction, which extracts features from a shrunk trace; (iii) data partitioning, which divides features into smaller pieces; (iv) DNS detection phase, which detects bots based on DNS features; and (v) TCP detection phase, which detects bots based on TCP features. The detection phases, which consume approximately 90% of the total detection time, can be dispatched to multiple servers in parallel and make detection in real-time. The large scale experiments with the Windows Azure cloud service show that BBDP achieves a high true positive rate (95%+) and a low false positive rate (approximate to 3%). Meanwhile, experiments also show that the performance of BBDP can scale up linearly with the number of servers used to detect bots. Copyright (c) 2013 John Wiley & Sons, Ltd.
URI: http://dx.doi.org/10.1002/sec.898
http://hdl.handle.net/11536/123955
ISSN: 1939-0114
DOI: 10.1002/sec.898
期刊: SECURITY AND COMMUNICATION NETWORKS
Volume: 7
Issue: 11
起始頁: 1849
結束頁: 1859
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