Feature evaluation for web crawler detection with data mining techniques

Distributed Denial of Service (DDoS) is one of the most damaging attacks on the Internet security today. Recently, malicious web crawlers have been used to execute automated DDoS attacks on web sites across the WWW. In this study we examine the effect of applying seven well-established data mining classification algorithms on static web server access logs in order to: (1) classify user sessions as belonging to either automated web crawlers or human visitors and (2) identify which of the automated web crawlers sessions exhibit ‘malicious’ behavior and are potentially participants in a DDoS attack. The classification performance is evaluated in terms of classification accuracy, recall, precision and F1 score. Seven out of nine vector (i.e. web-session) features employed in our work are borrowed from earlier studies on classification of user sessions as belonging to web crawlers. However, we also introduce two novel web-session features: the consecutive sequential request ratio and standard deviation of page request depth. The effectiveness of the new features is evaluated in terms of the information gain and gain ratio metrics. The experimental results demonstrate the potential of the new features to improve the accuracy of data mining classifiers in identifying malicious and well-behaved web crawler sessions.