Development of a multi-layered botmaster based analysis framework
1 online resource (131 pages) : PDF
University of North Carolina at Charlotte
Botnets are networks of compromised machines called bots that come together to form the tool of choice for hackers in the exploitation and destruction of computer networks. Most malicious botnets have the ability to be rented out to a broad range of potential customers, with each customer having an attack agenda different from the other. The result is a botnet that is under the control of multiple botmasters, each of which implement their own attacks and transactions at different times in the botnet. In order to fight botnets, details about their structure, users, and their users motives need to be discovered. Since current botnets require the information about the initial bootstrapping of a bot to a botnet, the monitoring of botnets are possible. Botnet monitoring is used to discover the details of a botnet, but current botnet monitoring projects mainly identify the magnitude of the botnet problem and tend to overt some fundamental problems, such as the diversified sources of the attacks. To understand the use of botnets in more detail, the botmasters that command the botnets need to be studied. In this thesis we focus on identifying the threat of botnets based on each individual botmaster. We present a multi-layered analysis framework which identifies the transactions of each botmaster and then we correlate the transactions with the physical evolution of the botnet. With these characteristics we discover what role each botmaster plays in the overall botnet operation. We demonstrate our results in our system: MasterBlaster, which discovers the level of interaction between each botmaster and the botnet. Our system has been evaluated in real network traces. Our results show that investigating the roles of each botmaster in a botnet should be essential and demonstrates its potential benefit for identifying and conducting additional research on analyzing botmaster interactions. We believe our work will pave the way for more fine-grained analysis of botnets which will lead to better protection capabilities and more rapid attribution of cyber crimes committed using botnets.
ATTRIBUTIONBOTNETBOTNET ANALYSISCYBERCRIMEDDOS DEFENSENETWORK MONITORING
Shehab, MohamedSaydam, CemStavrakas, Nickolas
Thesis (Ph.D.)--University of North Carolina at Charlotte, 2011.
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