Artificial immune system inspired behavior-based anti-spam filter

Authors:
Xun Yue_aff1n2;Ajith Abraham;Zhong-Xian Chi;Yan-You Hao;Hongwei Mo
Affiliations:
af2 Shandong Agricultural University, College of Information Sciences and Engineering, 271018, Taian, China;Chung-Ang University, IITA Professorship Program, School of Computer Science and Engineering, 221, Heukseok-dong, 156–756, Dongjak-gu Seoul, Republic of Korea;af1 Dalian University of Technology, Department of Computer Science and Engineering, 116024, Dalian, China;af1 Dalian University of Technology, Department of Computer Science and Engineering, 116024, Dalian, China;Harbin Engineering University, Automation College, 221, Heukseok-dong, 150001, Harbin, China
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Web intelligence and change discovery
Year:
2007

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Abstract

This paper proposes a novel behavior-based anti-spam technology for email service based on an artificial immune-inspired clustering algorithm. The suggested method is capable of continuously delivering the most relevant spam emails from the collection of all spam emails that are reported by the members of the network. Mail servers could implement the anti-spam technology by using the “black lists” that have been already recognized. Two main concepts are introduced, which defines the behavior-based characteristics of spam and to continuously identify the similar groups of spam when processing the spam streams. Experiment results using real-world datasets reveal that the proposed technology is reliable, efficient and scalable. Since no single technology can achieve one hundred percent spam detection with zero false positives, the proposed method may be used in conjunction with other filtering systems to minimize errors.