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Improving malware detection time by using RLE and N-gram

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

Malware is a widespread problem and despite the common use of anti-virus software, the diversity of malware is still increasing. A major challenge facing the anti-virus industry is how to effectively detect thousands of malware samples that are received every day. In this paper, a novel approach based Run Length Encoding (RLE) algorithm and n-gram are proposed to improve malware detect on dynamic analysis of based on API sequences.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Publication milestones

  • Published - 26/10/2017

Publication status

Published - 26/10/2017

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9780701702618

Publication IDs

  • handle.net: 10547/624314
  • Scopus: 85040004533

Host publication title

2017 23rd International Conference on Automation and Computing (ICAC)

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