An efficient deep learning-based scheme for web spam detection in IoT environment

From the last few years, Internet of Things has revolutionized the entire world. In this, various smart objects perform the tasks of sensing and computing to provide uninterrupted services to the end users in different applications such as smart transportation, e-healthcare to name a few. With the inherent capabilities of these objects to take adaptive intelligent decisions, Cognitive Internet of Things is another paradigm of Internet of Things which emerges during this era. However, while accessing data from the Internet, web spam is one of the challenges to be handled. It has been observed from the literature review that for accessing data, search engines are preferred mostly by an individual. The search engine’s effective ranking can decrease the computational cost of objects during the data access. The current solutions to this issue are aimed to discover the spam in the search engine after its occurrence. So, in this proposal, we present a cognitive spammer framework that removes spam pages when search engines calculate the web page rank score. The framework detects web spam with the support of Long Short-Term Memory network by training the link features. This training resulted with an accuracy of 95.25, as more than 1,11,000 hosts are being correctly classified. However, the content features are trained by neural network. The proposed scheme has been validated with the WEBSPAM-UK 2007 dataset. Prior to processing, the dataset is pre-processed using a new technique called ‘Split by Over-sampling and Train by Under-fitting’. The ensemble and cross validation approach has been used for optimization of results with an accuracy of 96.96%. So, the proposed scheme outperforms the existing techniques.