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

作者：Ruan Guangchen; Tan Ying^*

来源：Soft Computing, 2010, 14(2): 139-150.

DOI：10.1007/s00500-009-0440-2

• Spam detection
• Feature construction
• Concentration
• Artificial Immune System
• Neural networks

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.

• 出版日期2010-1
• 单位北京大学