Adaptive spam mail filtering using genetic algorithm

In this paper, we propose a mechanism for filtering incoming spam mails by generating spam mail prototypes using genetic algorithm. Firstly, words from e-mails are extracted and are categorized by their relating meaning into 7 groups. Then, we compose a string of chromosome having 7 genes, i.e., groups of words. Each gene, represented words in each group, is encoded into binary value. The genetic algorithm and its operations are applied to create varieties of spam mail prototypes which inherit from old spam mails. It saves time for preparing training sets and need no large training set for learning like other methods. The spam mail prototypes are the result of this learning mechanism. The experimental results show that the proposed system has efficiency. When testing with both spams and hams, the accuracy is about 85% in average.