A collaborative anti-spam system

Authors:
Gu-Hsin Lai;Chia-Mei Chen;Chi-Sung Laih;Tsuhan Chen
Affiliations:
Department of Information Management, National Sun Yat-Sen University, 70 Lien-hai Road, Kaohsiung 804, Taiwan, ROC;Department of Information Management, National Sun Yat-Sen University, 70 Lien-hai Road, Kaohsiung 804, Taiwan, ROC;Department of Electrical Engineering, National Cheng Kung University, 1 University Road, Tainan 701, Taiwan, ROC;Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 7
Cited 5

Evaluating cost-sensitive Unsolicited Bulk Email categorization

Proceedings of the 2002 ACM symposium on Applied computing
Rough sets and intelligent data analysis

Information Sciences—Informatics and Computer Science: An International Journal
Boolean Reasoning Scheme with Some Applications in Data Mining

PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A Rough Set-Based Approach to Text Classification

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Identifying Junk Electronic Mail in Microsoft Outlook with a Support Vector Machine

SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
A Neural Network Based Approach to Automated E-Mail Classification

WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Support vector machines for spam categorization

IEEE Transactions on Neural Networks

Symbiotic filtering for spam email detection

Expert Systems with Applications: An International Journal
Facing the spammers: A very effective approach to avoid junk e-mails

Expert Systems with Applications: An International Journal
Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification

Applied Soft Computing
Grindstone4Spam: An optimization toolkit for boosting e-mail classification

Journal of Systems and Software
Automated crime report analysis and classification for e-government and decision support

Proceedings of the 14th Annual International Conference on Digital Government Research

Quantified Score

Hi-index	12.05

Visualization

Abstract

Growing volume of spam mails has generated a need for a precise anti-spam filter detecting unsolicited emails. Most works only focus on spam rule generation on a standalone mail server. This paper presents a collaborative framework on spam rule generation, exchange and management. The spam filter can be built based on the mixture of rough set theory, genetic algorithm, and reinforcement learning. In this paper, we use rough set theory to generate spam rules and XML format for exchanging spam rules. The spam rule management is achieved by reinforcement learning approach. The results of experiment draw the following conclusion: (1) Rule management can keep high performance rules and discard out-of-date rules to improve the accuracy and efficiency of spam filter. (2) Rules exchanged among mail servers indeed help the spam filter block more spam messages than standalone one.