An Intelligent System for Spam Detection and Identification of the most Relevant Features based on Evolutionary Random Weight Networks
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With the incremental use of e-mails as an essential and popular communication means over the Internet, there comes a serious threat that impacts the Internet and society. This problem is known as spam. By receiving spam messages, Internet users are exposed to security issues, and minors are exposed to inappropriate contents. Moreover, spam messages waste resources in terms of storage, bandwidth and productivity. What makes the problem worse is that spammers keep inventing new techniques to dodge spam filters. On the other side, the massive data flows of hundreds of millions of individuals and a large number of attributes make the problem more cumbersome and complex. Therefore, proposing evolutionary and adaptable spam detection models is a necessity. In this paper, an intelligent detection system is proposed based on Genetic Algorithm (GA) and Random Weight Network (RWN) to deal with Email spam detection tasks. In addition, an automatic identification capability is also embedded in the proposed system to detect the most relevant features during the detection process. The proposed system is intensively evaluated through a series of extensive experiments based on three email corpora. The experimental results confirm that the proposed system can achieve remarkable results in terms of accuracy, precision, and recall. Furthermore, the proposed detection system can successfully discover the most relevant features of the spam emails. For more info, please refer to aliasgharheidari.com
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