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Botnets are now recognized as one of the most serious security threats. In contrast to previous malware, botnets have the characteristic of a command and control (C&C) channel. Botnets also often use existing common protocols, e.g., IRC, HTTP, and in protocol-conforming manners. This makes the detection of botnet C&C a challenging problem. In this paper, we propose an approach that uses network-based anomaly detection to identify botnet C&C channels in a local area network without any prior knowl- edge of signatures or C&C server addresses. This detection approach can identify both the C&C servers and infected hosts in the network. Our approach is based on the observa- tion that, because of the pre-programmed activities related to C&C, bots within the same botnet will likely demonstrate spatial-temporal correlation and similarity. For example, they engage in coordinated communication, propagation, and attack and fraudulent activities. Our prototype system,SpyBot, can capture this spatial-temporal correlation in network traf?c and utilize statistical algorithms to detect botnets with theoretical bounds on the false positive and false negative rates. We evaluated SpyBot using many real- world network traces. The results show that SpyBot can detect real-world botnets with high accuracy and has a very low false positive rate.
[Raagavi P, Palaniyapan (2015); Spy Bot- Bot Net Detection Int. J. of Adv. Res. 3 (2). 0] (ISSN 2320-5407). www.journalijar.com
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