Spam Identification in Cloud Computing Based on Text Filtering System

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With the rapid increase of spam on the Internet and the diversification of its forms, how to quickly and effectively identify a large number of spam on the Internet has become an urgent topic. Cloud computing has obvious advantages in storage and processing, so it can effectively calculate a large amount of mail data. Due to the uncertainty and life cycle of spam, feedback re-judgment is added to the anti-spam system, and a text filtering system based on active learning with four stages of training, filtering, feedback, and re-filtering is implemented. Compared with the original system, the filtering system with feedback can improve the filtering of keywords. In order to effectively reduce the misjudgment rate of ordinary mail and improve the accuracy of spam judgment, it is suggested to improve the use of weighted decision-making of email header information to implement effective auxiliary classification. For emails lacking content, the filtering method of title weighting is feasible and effective, which can improve the identification of spam with relatively little text content. Because the filtering method on the cloud is far more advanced than the traditional algorithm, the development of the Internet can effectively solve the infinite increase of spam. Therefore, this paper makes an in-depth study on spam identification in cloud computing based on text filtering system by summarizing and analyzing the current anti-spam technologies.