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Botnets have become one of the major threats on the Internet for serving as a vector for carrying attacks against organizations and committing cybercrimes. They are used to generate spam, carry out DDOS attacks and click-fraud, and steal sensitive information. In this paper, we propose a new approach for characterizing and detecting botnets using network traffic behaviors. Our approach focuses on detecting the bots before they launch their attack. We focus in this paper on detecting P2P bots, which represent the newest and most challenging types of botnets currently available. We study the ability of five different commonly used machine learning techniques to meet on line botnet detection requirements, namely adaptability, novelty detection, and early detection. The results of our experimental evaluation based on existing datasets show that it is possible to detect effectively botnets during the botnet Command-and Control (C&C) phase and before they launch their attacks using traffic behaviors only. However, none of the studied techniques can address all the above requirements at once.

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Botnets are becoming the predominant threat on the Internet today and is the primary vector for carrying out attacks against organizations and individuals. Botnets have been used in a variety of cybercrime, from click-fraud to DDOS attacks to the generation of spam. In this paper we propose an approach to detect botnet activity by classifying network traffic behavior using machine learning classification techniques. We study the feasibility of detecting botnet activity without having seen a complete network flow by classifying behavior based on time intervals and we examine the performance of two popular classification techniques with respect to this data. Using existing datasets, we show experimentally that it is possible to identify the presence of botnet activity with high accuracy even with very small time windows, though there are some limitations to the approach based on the selection of attributes.

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