An automated classification system based on the strings of trojan and virus families
Tian, Ronghua, Batten, Lynn, Islam, Rafiqul and Versteeg, Steve 2009, An automated classification system based on the strings of trojan and virus families, in MALWARE 2009: 4th International Conference on Malicious and Unwanted Software, IEEE, New York, N.Y., pp. 23-30.
Classifying malware correctly is an important research issue for anti-malware software producers. This paper presents an effective and efficient malware classification technique based on string information using several wellknown classification algorithms. In our testing we extracted the printable strings from 1367 samples, including unpacked trojans and viruses and clean files. Information describing the printable strings contained in each sample was input to various classification algorithms, including treebased classifiers, a nearest neighbour algorithm, statistical algorithms and AdaBoost. Using k-fold cross validation on the unpacked malware and clean files, we achieved a classification accuracy of 97%. Our results reveal that strings from library code (rather than malicious code itself) can be utilised to distinguish different malware families.
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Field of Research
080403 Data Structures
Socio Economic Objective
890205 Information Processing Services (incl. Data Entry and Capture)
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