Classification of malware based on string and function feature selection
Islam, Rafiqul, Tian, Ronghua, Batten, Lynn and Versteeg, Steve 2010, Classification of malware based on string and function feature selection, in CTC 2010 : Proceedings of the Second Cybercrime and Trustworthy Computing Workshop 2010, IEEE, Piscataway, N.J., pp. 9-17.
(Some files may be inaccessible until you login with your DRO credentials)
Anti-malware software producers are continually challenged to identify and counter new malware as it is released into the wild. A dramatic increase in malware production in recent years has rendered the conventional method of manually determining a signature for each new malware sample untenable. This paper presents a scalable, automated approach for detecting and classifying malware by using pattern recognition algorithms and statistical methods at various stages of the malware analysis life cycle. Our framework combines the static features of function length and printable string information extracted from malware samples into a single test which gives classification results better than those achieved by using either feature individually. In our testing we input feature information from close to 1400 unpacked malware samples to a number of different classification algorithms. Using k-fold cross validation on the malware, which includes Trojans and viruses, along with 151 clean files, we achieve an overall classification accuracy of over 98%.
Field of Research
080303 Computer System Security
Socio Economic Objective
890206 Internet Hosting Services (incl. Application Hosting Services)
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO.
If you believe that your rights have been infringed by this repository, please contact email@example.com.