By Topic

Email worm detection by wavelet analysis of DNS query streams

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Nikolaos Chatzis ; Fraunhofer Institute FOKUS, Kaiserin-Augusta-Allee 31, 10589 Berlin, Germany ; Radu Popescu-Zeletin ; Nevil Brownlee

The high prevalence of email worms indicates that current in-network defence mechanisms are incapable of mitigating this Internet threat. Moreover, commonly applied approaches against this class of propagating malicious program do not target reducing unwanted email traffic traversing the Internet. In this paper, we take a step toward better understanding of email worms, and explore their effect on the flow-level characteristics of domain name system (DNS) query streams that user machines generate. We propose a novel method, which uses time series analysis and unsupervised learning, to detect email worms as they appear on local name servers. To evaluate our detection method, we have constructed a DNS query dataset that consists of 71 email worms. We demonstrate that our method is very effective.

Published in:

Computational Intelligence in Cyber Security, 2009. CICS '09. IEEE Symposium on

Date of Conference:

March 30 2009-April 2 2009