Data Mining Methods for Detection of New Malicious Executables

@inproceedings{Schultz2001DataMM,
  title={Data Mining Methods for Detection of New Malicious Executables},
  author={Matthew G. Schultz and Eleazar Eskin and Erez Zadok and Salvatore J. Stolfo},
  booktitle={IEEE Symposium on Security and Privacy},
  year={2001}
}
A serious security threat today is malicious executables, especially new, unseen malicious executables often arriving as email attachments. These new malicious executables are created at the rate of thousands every year and pose a serious security threat. Current anti-virus systems attem pt to detect these new malicious programs with heuristics generated by hand. This approach is costly and oftentimes ineffective. In this paper, we present a data-mining framework that detects new, previously… CONTINUE READING
Highly Influential
This paper has highly influenced 59 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 784 citations. REVIEW CITATIONS
475 Citations
31 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 475 extracted citations

785 Citations

050'02'05'09'13'17
Citations per Year
Semantic Scholar estimates that this publication has 785 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 31 references

Machine Learning

  • Tom Mitchell
  • 1997
Highly Influential
5 Excerpts

Automatic Extraction of Computer Virus Signatures

  • Jeffrey O. Kephart, William C. Arnold
  • 4th Virus Bulletin International Conference ,
  • 1994
Highly Influential
4 Excerpts

Microsoft Hack Shows Companies Are Vulnerable

  • H. Eugene
  • 2000

Virus descriptions of viruses in the wild

  • Wildlist Organization
  • Online publication,
  • 2000
2 Excerpts

http://www.pcug.org.au/ millerp/hexdump.html

  • Peter Miller
  • Hexdump.Online publication,
  • 2000
1 Excerpt

Kephart and William C . Arnold . Automatic Extraction of Computer Virus Signatures

  • O. Jeffrey
  • 1999

Similar Papers

Loading similar papers…