Classification methods in the detection of new malicious emails

Authors:
Dong-Her Shih;Hsiu-Sen Chiang;C. David Yen
Affiliations:
Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Touliu, Yunlin, Taiwan, ROC;Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Touliu, Yunlin, Taiwan, ROC;Department of DSC & MIS Miami University, Oxford, OH 45056, USA
Venue:
Information Sciences: an International Journal
Year:
2005

Citing 10
Cited 3

Introduction to probability and statistics (7th ed.)

Introduction to probability and statistics (7th ed.)
Machine learning of rules and trees

Machine learning, neural and statistical classification
Data mining: concepts and techniques

Data mining: concepts and techniques
Melissa Virus Creates a New Type of Threat

Computer
Attacking Malicious Code: A Report to the Infosec Research Council

IEEE Software
MET: an experimental system for Malicious Email Tracking

Proceedings of the 2002 workshop on New security paradigms
The Application of CMAC Based Intelligent Agents in the Detection of Previously Unseen Computer Viruses

ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Data Mining Methods for Detection of New Malicious Executables

SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
A Generic Virus Detection Agent on the Internet

HICSS '97 Proceedings of the 30th Hawaii International Conference on System Sciences: Information Systems Track—Internet and the Digital Economy - Volume 4
Comparing Bayesian network classifiers

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence

Web-Based three-dimension e-mail traffic visualization

APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
Segmental parameterisation and statistical modelling of e-mail headers for spam detection

Information Sciences: an International Journal
A new adaptive decentralized soft decision combining rule for distributed sensor systems with data fusion

Information Sciences: an International Journal

Quantified Score

Hi-index	0.07

Visualization

Abstract

A serious security threat today is malicious emails, especially new, unseen Internet worms and viruses often arriving as email attachments. These new malicious emails are created at the rate of thousands every year. Current anti-virus systems attempt to detect these new malicious email viruses with signatures generated by hand but it is often times costly. In this paper, we present some classification methods that detect new, unseen malicious emails accurately and automatically. The classification method found discrepancy behaviors in data set and use these behaviors to detect new malicious email viruses. Comparison results show the accuracy in the detection of new malicious emails. In order to improve the detection accuracy, the prototype of the bagged classifier is utilized in the implementation of our malicious email detection system.