Detecting redirection spam using multilayer perceptron neural network

Quality information retrieval from Web is essential for every search engine. But the quality of information is being exploited by spammers who make heavy use of malicious redirections for the purpose of phishing, downloading malware or attaining high search engine ranking. Malicious redirections present the irrelevant content to search user, thereby affecting user satisfaction. It also leads to wastage of network bandwidth. In this paper, we propose a neural framework for detecting redirection spam. We incorporated the feed-forward multilayer perceptron network and used scaled conjugate gradient algorithm that is able to perform very fast classification of URLs leading to redirection spam. We investigated the network empirically to choose the number of hidden layers and observed that when network is trained with two hidden layers, it gives better accuracy. We validated our proposed approach against the dataset of 2383 URLs and were able to detect the spammed redirections with high accuracy. The results indicate that neural networks are very effective technique to model the redirection spam detection.