A survey of emerging approaches to spam filtering
ACM Computing Surveys (CSUR)
Hybrid email spam detection model with negative selection algorithm and differential evolution
Engineering Applications of Artificial Intelligence
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Nowadays, detecting and filtering are still the most feasible ways of fighting spam emails . There are many reasonably successful spam email filters in operation. However, proactively catching new strains of spam emails, where no previous knowledge is available, is still a major challenge. Negative selection is a branch of artificial immune systems. It has a strong temporal nature and is especially suitable for discovering unknown temporal patterns. This nature makes it a good candidate in quickly discovering and detecting new strains of spam emails. In this paper, we study the feasibility of negative selection in detecting spam emails without using any prior knowledge of any spam emails. We use TREC07 corpus for our experiments. The outcomes, under the assumption of no prior knowledge about spam emails, are very encouraging. We also discuss our findings and point out possible future directions.