FS2RNN: Feature Selection Scheme for Web Spam Detection Using Recurrent Neural Networks

In modern era, Internet plays a key role in accessing and fetching web information and web resources from World Wide Web (WWW). The websites act as a medium for retrieving information from the web. Although it increases the data retrieval and users interactions, it also opens the gate for various types of attacks. For example, spams in the websites attract various Internet users. It has been observed from the literature that many authors attempted to detect the web spam using various machine learning techniques. However, none of these techniques used deep learning architecture for detection of hidden patterns. Hence, in this paper, a deep learning algorithm, i.e., Recurrent Neural Networks (RNN), has been used for the classification of spam nodes. We devise here a framework called FS
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RNN: Feature Selection Scheme using Recurrent Neural Networks. In this framework, the dataset is preprocessed before applying RNN in which principal component analysis (PCA) is used for dimension reduction on the data set and recursive feature elimination (RFE) is used for feature selection. The accuracy of the proposed framework, when compared before and after preprocessing, is improved by 24.2 %, which is excellent result.