N-gram-based detection of new malicious code

@article{AbouAssaleh2004NgrambasedDO,
  title={N-gram-based detection of new malicious code},
  author={Tony Abou-Assaleh and Nick Cercone and Vlado Keselj and Ray Sweidan},
  journal={Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004.},
  year={2004},
  volume={2},
  pages={41-42 vol.2}
}
The current commercial anti-virus software detects a virus only after the virus has appeared and caused damage. Motivated by the standard signature-based technique for detecting viruses, and a recent successful text classification method, we explore the idea of automatically detecting new malicious code using the collected dataset of the benign and malicious code. We obtained accuracy of 100% in the training data, and 98% in 3-fold cross-validation. 
Highly Influential
This paper has highly influenced 11 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 174 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.
103 Citations
6 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 103 extracted citations

175 Citations

0102030'06'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 175 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-6 of 6 references

2003–04. Perl package Text::Ngrams. WWW: http://search. cpan.org/author/VLADO/Text- Ngrams-0.03/Ngrams.pm

  • V. Kešelj
  • 2003
1 Excerpt

Automatic Extraction of Computer Virus Signatures.

  • J. O. Kephart, W. C. Arnold
  • In Proc.of the 4th Virus Bulletin Int’l Conf…
  • 1994
2 Excerpts

Similar Papers

Loading similar papers…