Advertisement

Email Worm Detection Using Naïve Bayes and Support Vector Machine

  • Mohammad M. Masud
  • Latifur Khan
  • Ehab Al-Shaer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)

Abstract

Email worm, as the name implies, spreads through infected email messages. The worm may be carried by attachment, or the email may contain links to an infected website. When the user opens the attachment, or clicks the link, the host is immediately infected. Email worms use the vulnerability of the email software of the host machine and sends infected emails to the addresses stored in the address book. In this way, new machines get infected. Examples of email worms are “W32.mydoom.M@mm”, “W32.Zafi.d”, “W32.LoveGate.w”, “W32.Mytob.c”, and so on. Worms do a lot of harm to computers and people. They can clog the network traffic, cause damage to the system and make the system unstable or even unusable.

Keywords

Support Vector Machine Cross Validation False Negative Rate Network Traffic Support Vector Machine Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Martin, S., Sewani, A., Nelson, B., Joseph, K.C.A.D.: A Two-Layer Approach for Novel Email Worm Detection, http://www.cs.berkeley.edu/~anil/papers/SRUTI_submitted.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mohammad M. Masud
    • 1
  • Latifur Khan
    • 1
  • Ehab Al-Shaer
    • 2
  1. 1.Department of Computer ScienceThe University of Texas at DallasRichardson
  2. 2.School of Computer Science, Telecommunications and Information SystemsDePaul UniversityChicago

Personalised recommendations