A comparative study of malware family classification

MD Rafiqul Islam; Irfan Altas

doi:10.1007/978-3-642-34129-8_48

A comparative study of malware family classification

MD Rafiqul Islam, Irfan Altas

Research output: Book chapter/Published conference paper › Chapter (peer-reviewed) › peer-review

6 Citations (Scopus)

Abstract

In this paper, we present a comparative study of conventional malware family classification techniques and identifiy their limitations. In our study, we investigate three different feature set, function length frequency and printable string information as static features and Application Programming Interface (API) calls and API parameters as dynamic features. In our classification process, we used some of well-known machine-learning algorithms by invoking WEKA libraries. We made a comparative analysis and conclude that the independent features are not good enough to defence against current as well as future malware.

Original language	English
Title of host publication	Information and communications security
Subtitle of host publication	14th International Conference, ICICS 2012, Hong Kong, China, October 29-31, 2012. Proceedings
Editors	Tat Wing Chim, Tsz Hon Yuen
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	488-496
Number of pages	9
ISBN (Electronic)	9783642341298
ISBN (Print)	9783642341281
DOIs	https://doi.org/10.1007/978-3-642-34129-8_48
Publication status	Published - 2012

Publication series

Name	Lecture Notes in Computer Science
Volume	7618

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10.1007/978-3-642-34129-8_48

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Islam, MD. R., & Altas, I. (2012). A comparative study of malware family classification. In T. W. Chim, & T. H. Yuen (Eds.), Information and communications security: 14th International Conference, ICICS 2012, Hong Kong, China, October 29-31, 2012. Proceedings (pp. 488-496). (Lecture Notes in Computer Science; Vol. 7618). Springer. https://doi.org/10.1007/978-3-642-34129-8_48

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abstract = "In this paper, we present a comparative study of conventional malware family classification techniques and identifiy their limitations. In our study, we investigate three different feature set, function length frequency and printable string information as static features and Application Programming Interface (API) calls and API parameters as dynamic features. In our classification process, we used some of well-known machine-learning algorithms by invoking WEKA libraries. We made a comparative analysis and conclude that the independent features are not good enough to defence against current as well as future malware.",

author = "Islam, {MD Rafiqul} and Irfan Altas",

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Islam, MDR & Altas, I 2012, A comparative study of malware family classification. in TW Chim & TH Yuen (eds), Information and communications security: 14th International Conference, ICICS 2012, Hong Kong, China, October 29-31, 2012. Proceedings. Lecture Notes in Computer Science, vol. 7618, Springer, Berlin, Germany, pp. 488-496. https://doi.org/10.1007/978-3-642-34129-8_48

A comparative study of malware family classification. / Islam, MD Rafiqul ; Altas, Irfan.
Information and communications security: 14th International Conference, ICICS 2012, Hong Kong, China, October 29-31, 2012. Proceedings. ed. / Tat Wing Chim; Tsz Hon Yuen. Berlin, Germany: Springer, 2012. p. 488-496 (Lecture Notes in Computer Science; Vol. 7618).

Research output: Book chapter/Published conference paper › Chapter (peer-reviewed) › peer-review

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AU - Altas, Irfan

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AB - In this paper, we present a comparative study of conventional malware family classification techniques and identifiy their limitations. In our study, we investigate three different feature set, function length frequency and printable string information as static features and Application Programming Interface (API) calls and API parameters as dynamic features. In our classification process, we used some of well-known machine-learning algorithms by invoking WEKA libraries. We made a comparative analysis and conclude that the independent features are not good enough to defence against current as well as future malware.

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A comparative study of malware family classification

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