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Author Younghee Park ♦ Qinghua Zhang ♦ Reeves, D. ♦ Mulukutla, V.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Semantics ♦ Malware ♦ Engines ♦ Data mining ♦ Software ♦ Registers ♦ Clustering algorithms ♦ Data Mining ♦ Malware ♦ BotNet ♦ Static Analysis
Abstract Among malicious software (malware), autonomous malicious programs, called bots, are a serious problem in the Internet. The bot writers have developed a variety of techniques to evade simple signature-based detection. Concise representations of malware behavior, or semantic patterns, are much harder to evade or obfuscate. However, generating a semantic pattern for every program instance is time-consuming, and comparing with a large number of patterns creates a challenge for timely identification of bots. This paper proposes an automated approach to generate semantic patterns for bot detection. Unlike previous approaches, it is intended to find one pattern that accurately represents the important behavior of an entire class of bots, rather than of individual instances. Doing so has advantages for fast malware identification, and for distinguishing new classes of attacks from previously-seen attacks. The work uses static analysis to characterize bot behaviors, and proposes to use hierarchical clustering of the resulting semantic patterns from a set of bot programs. The goal is to identify critical, common semantic behavior that represents the functions of an entire class of the malware. This method has been prototyped and evaluated on real-world malicious bot software. Depending on parameter choices, our approach can achieve more than 95% detection rates and less than 5% false positive rates on a large set of bot programs and non-bot executables.
ISBN 9781424475124
ISSN 07303157
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-07-19
Publisher Place Korea (South)
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424475131
Size (in Bytes) 608.29 kB
Page Count 11
Starting Page 262
Ending Page 272

Source: IEEE Xplore Digital Library