Feature selection and similarity coefficient based method for email spam filtering

Many threats in the real world can be related to activities of persons on the Internet. Spam is one of the most pressing security problems online. Spam filters try to identify likely spam either manually or automatically. Most of the spam datasets used in the spam filtering area of study deal with large amounts of data containing irrelevant and/or redundant features. This redundant information has a negative impact on the accuracy and detection rate of many methods that have been used for detection and filtering. In this study, statistical feature selection approach combined with similarity coefficients are used to improve the accuracy and detection rate for the spam detection and filtering. At the end, the study results based on email spam datasets show that our proposed approach enhanced the detection rate, false alarm rate and the accuracy.

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