Design and Implementation of a Data Mining System for Malware Detection

Article type: Research Article

Authors: Thuraisingham, Bhavani | Al-Khatib, Tahseen | Khan, Latifur | Masud,, Mehedy | Hamlen, Kevin | Khadilkar, Vaibhav | Abrol, Satyen

Affiliations: Computer Science Department, The University of Texas at Dallas, Dallas, TX, USA

Note: [] Corresponding author. E-mail: bhavani.thuraisingham@utdallas.edu; Tel: (+1) 972-883-4738

Abstract: This paper describes the design and implementation of a data mining system called SNODMAL (Stream based novel class detection for malware) for malware detection. SNODMAL extends our data mining system called SNOD (Stream-based Novel Class Detection) for detecting malware. SNOD is a powerful system as it can detect novel classes. We also describe the design of SNODMAL++ which is an extended version of SNODMAL.

Keywords: Data mining, malware detection, machine learning, stream-based novel class detection, streambased classification

DOI: 10.3233/jid-2012-0016

Journal: Journal of Integrated Design & Process Science, vol. 16, no. 2, pp. 33-49, 2012