Design and Implementation of a Data Mining System for Malware Detection

Authors:
Bhavani Thuraisingham;Tahseen Al-Khatib;Latifur Khan;Mehedy Masud,;Kevin Hamlen;Vaibhav Khadilkar;Satyen Abrol
Affiliations:
Computer Science Department, The University of Texas at Dallas, Dallas, TX, USA;Computer Science Department, The University of Texas at Dallas, Dallas, TX, USA;Computer Science Department, The University of Texas at Dallas, Dallas, TX, USA;Computer Science Department, The University of Texas at Dallas, Dallas, TX, USA;Computer Science Department, The University of Texas at Dallas, Dallas, TX, USA;Computer Science Department, The University of Texas at Dallas, Dallas, TX, USA;Computer Science Department, The University of Texas at Dallas, Dallas, TX, USA
Venue:
Journal of Integrated Design & Process Science
Year:
2012

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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.