City Research Online

An Experimental Study of Diversity with Off-The-Shelf AntiVirus Engines

Gashi, I., Stankovic, V. ORCID: 0000-0002-8740-6526, Leita, C. & Thonnard, O. (2009). An Experimental Study of Diversity with Off-The-Shelf AntiVirus Engines. In: 2009 8TH IEEE INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS. Eighth IEEE International Symposium on Network Computing and Applications, 9 - 11 July 2009, Cambridge, MA, USA.

Abstract

Fault tolerance in the form of diverse redundancy is well known to improve the detection rates for both malicious and non-malicious failures. What is of interest to designers of security protection systems are the actual gains in detection rates that they may give. In this paper we provide exploratory analysis of the potential gains in detection capability from using diverse AntiVirus products for the detection of self-propagating malware. The analysis is based on 1599 malware samples collected by the operation of a distributed honeypot deployment over a period of 178 days. We sent these samples to the signature engines of 32 different AntiVirus products taking advantage of the VirusTotal service. The resulting dataset allowed us to perform analysis of the effects of diversity on the detection capability of these components as well as how their detection capability evolves in time.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Publisher Keywords: AntiVirus detection engine analysis, malware detection, cluster analysis
Subjects: Q Science > QA Mathematics > QA76 Computer software
Departments: School of Science & Technology > Computer Science > Software Reliability
[thumbnail of NCA09_AV_paper.pdf]
Preview
PDF
Download (873kB) | Preview

Export

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login