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Novel malware detection methods by using LCS and LCSS

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

Open access

Abstract

The field of computer security faces numerous vulnerabilities which cause network resources to become unavailable and violate systems confidentiality and integrity. Malicious software (Malware) has become one of the most serious security threats on the Internet. 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 on dynamic analysis of malware is proposed whereby Longest Common Subsequence (LCSS) and Longest Common Substring (LCS) algorithms are adopted to accurately detect malware. The empirical results show that the proposed approach performs favorably compared to other related work that use API call sequences.

Publication Information

Output type

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

Original language

English

Pages from-to (Number of pages)

Pages 554-559

Publication milestones

  • Published - 24/10/2016

Publication status

Published - 24/10/2016

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781862181311

ISBN (Electronic)

9781862181328

Publication IDs

  • handle.net: 10547/622051
  • Scopus: 84999009610

Host publication title

Proceedings of The 22nd IEEE International Conference on Automation & Computing

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