Anomaly-Based Detection of IRC Botnets by Means of One-Class Support Vector Classifiers
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Botnet: classification, attacks, detection, tracing, and preventive measures
ICICIC '09 Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control
Simulation-based study of botnets and defense mechanisms against them
Journal of Computer and Systems Sciences International
Survey and taxonomy of botnet research through life-cycle
ACM Computing Surveys (CSUR)
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Networked hosts' vulnerabilities pose some serious threats to the operation of computer networks. Modern attacks are increasingly complex, and exploit many strategies in order to perform their intended malicious tasks. Attackers have developed the ability of controlling large sets of infected hosts, characterized by complex executable command sets, each taking part incooperative and coordinated attacks. There are many ways to perform control onan \emph{army} of possibly unaware infected hosts, and an example of such techniques is discussed in this paper. We will address the problem of detecting \emph{botnets}, by introducing a network traffic analysis architecture, and describing a behavioral model, for a specific class of network users, capable of identifying \emph{botnet}-related activities.