Title

A Survey Of Data Mining Techniques For Malware Detection Using File Features

Keywords

Data mining; Instruction sequences; Machine learning; Malware detection; N-grams; Survey; System calls

Abstract

This paper presents a survey of data mining techniques for malware detection using file features. The techniques are categorized based upon a three tier hierarchy that includes file features, analysis type and detection type. File features are the features extracted from binary programs, analysis type is either static or dynamic, and the detection type is borrowed from intrusion detection as either misuse or anomaly detection. It provides the reader with the major advancement in the malware research using data mining on file features and categorizes the surveyed work based upon the above stated hierarchy. This served as the major contribution of this paper.

Publication Date

1-1-2008

Publication Title

Proceedings of the 46th Annual Southeast Regional Conference on XX, ACM-SE 46

Number of Pages

509-510

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1593105.1593239

Socpus ID

73049094155 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/73049094155

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