By Wenke Lee, Cliff Wang, David Dagon
Botnets became the platform of selection for launching assaults and committing fraud on the net. a greater figuring out of Botnets can assist to coordinate and enhance new applied sciences to counter this severe safety chance. Botnet Detection: Countering the most important defense chance includes chapters contributed through world-class leaders during this box, from the June 2006 ARO workshop on Botnets. This edited quantity represents the state of the art in study on Botnets.
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Extra info for Botnet Detection: Countering the Largest Security Threat (Advances in Information Security)
Timothy Strayer, David Lapsely, Robert Walsh, and Carl Livadas 22. National Infrastructure Security Coordination Center. Targeted trojan email attacks. NISCC Briefing 08/2005, June 16, 2005. 23. Anirudh Ramachandran, Nick Feamster, and David Dagon. Revealing botnet membership using DNSBL counter-intelligence. In Proceedings of the 2nd Workshop on Steps to Reducing Unwanted Traffic on the Internet (SRUTI), 2006. 24. Matthew Roughan, Subhabrata Sen, Oliver Spatscheck, and Nick Duffield. Class-ofservice mapping for qos: a statistical signature-based approach to ip traffic classification.
One plausible explanation for this is based on the law of rare events. Usually the botnet scans a large IP scope, and the sensor is only a tiny portion of it. If the botnet uses random scanning, for each scan session there is a small probability p to arrive the honeynet detection sensor. According the law of rare events, the observed scan sessions in a given time interval will follow a Poisson distribution and the inter arrival time will follow an exponential distribution. 0 Fig. 5. The cumulative scan session arrival process of the event on TCP port 8888 on 200602-06, which corresponding to a backdoor shell.
Section 3 describes our experimental method, and Sect. 4 details our prototype implementation. Our experimental results are given in Sect. 5. We discuss the potential for and challenges to applying our findings for real-time host-based bot detection in Sect. 6. Section 7 describes related work and Sect. 8 provides concluding remarks. 1 Bot Families and Variants The Honeynet Project identifies four main Win32 bot families: (1) agobot – the most well known; (2) sdbot – the most common; (3) DSNXbot; and (4) mIRC-based 48 Elizabeth Stinson and John C.