Design and implementation of a data mining system for malware detection

Bhavani Thuraisingham; Tahseen Al-Khatib; Latifur Khan; Mehedy Masud; Kevin Hamlen; Vaibhav Khadilkar; Satyen Abrol

doi:10.3233/jid-2012-0016

Design and implementation of a data mining system for malware detection

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

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	33-49
Number of pages	17
Journal	Journal of Integrated Design and Process Science
Volume	16
Issue number	2
DOIs	https://doi.org/10.3233/jid-2012-0016
Publication status	Published - Dec 1 2012
Externally published	Yes

Keywords

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

ASJC Scopus subject areas

Engineering(all)

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10.3233/jid-2012-0016

Cite this

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AU - Hamlen, Kevin

AU - Khadilkar, Vaibhav

AU - Abrol, Satyen

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AB - 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.

KW - Data mining

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KW - streambased classification

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Design and implementation of a data mining system for malware detection

Abstract

Keywords

ASJC Scopus subject areas

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