A three-layer back-propagation neural network for spam detection using artificial immune concentration

Authors:
Guangchen Ruan;Ying Tan
Affiliations:
Peking University, Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, School of Electronics Engineering and Computer Science, 100871, Beijing, China;Peking University, Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, School of Electronics Engineering and Computer Science, 100871, Beijing, China
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Pattern Recognition and Information Processing Using Neural Networks;Guest Editors: Fuchun Sun,Ying Tan,Cong Wang
Year:
2009

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ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I

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Abstract

In this paper, a three-layer back-propagation neural network (BPNN) is employed for spam detection by using a concentration based feature construction (CFC) approach. In the CFC approach, ‘self’ and ‘non-self’ concentrations are constructed through ‘self’ and ‘non-self’ gene libraries, respectively, to form a two-element concentration vector for expressing the e-mail efficiently. A three-layer BPNN with two-element input is then employed to classify e-mails automatically. Comprehensive experiments are conducted on two public benchmark corpora PU1 and Ling to demonstrate that the proposed CFC approach based BPNN classifier not only has a very much fast speed but also achieves 97 and 99% of classification accuracy on corpora PU1 and Ling by just using a two-element concentration feature vector.