Identifying Text Spam Based on Supervised Machine Learning Models: A Review

Web users are continuously bombarded with spamming attacks. Sometimes, such spamming attacks may successfully re-direct the user to a malicious web link which is generally termed as re-direction spam. However, genuine re-directions are also common in case the web-servers are overloaded with requests which are more that that can be processed. It can be challenging to distinguish redirection spam from actual web-redirections. Lately, artificial intelligence has been used for redirection spam classification using the design of various models of artificial neural networks. The performance parameters are generally the accuracy and mean square error. This paper presents a comprehensive survey on redirection spamming attack detection using artificial intelligence based approaches so as to thwart spamming attacks for time critical applications. Various models have been discussed with their pros and cons.