An Analysis of Machine Learning Methods for Spam Host Detection

The web is becoming an increasingly important source of entertainment, communication, research, news and trade. In this way, the web sites compete to attract the attention of users and many of them achieve visibility through malicious strategies that try to circumvent the search engines. Such sites are known as web spam and they are generally responsible for personal injury and economic losses. Given this scenario, this paper presents a comprehensive performance evaluation of several established machine learning techniques used to automatically detect and filter hosts that disseminate web spam. Our experiments were diligently designed to ensure statistically sounds results and they indicate that bagging of decision trees, multilayer perceptron neural networks, random forest and adaptive boosting of decision trees are promising in the task of web spam classification and, hence, they can be used as a good baseline for further comparison.