Individual spam filtering algorithm based on immune principles

With the inspiration from self-protection mechanism of biological immune system, an individual spam filtering algorithm based on immune principles is proposed. Firstly, the spam communities are defined according to the users' interests and the email features. Then all spams are classified into different spam communities. Secondly, the community features are extracted and represented by a set of feature detectors. Finally, the identification of a spam depends on whether the email can be classified into any spam community. The proposed algorithm is an incremental learning algorithm and it can continuously filter spam without retraining. The immune learning and immune memory mechanisms adopted in this algorithm improve not only the detectable rate and the accuracy rate but also the filter speed. Experimental results show that the algorithm is better than the AISEC algorithm and the Naive Bayesian algorithm.