C4.5: programs for machine learning
C4.5: programs for machine learning
Discovery of Web Robot Sessions Based on their Navigational Patterns
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Web robot detection: A probabilistic reasoning approach
Computer Networks: The International Journal of Computer and Telecommunications Networking
Web robot detection techniques: overview and limitations
Data Mining and Knowledge Discovery
Hi-index | 0.00 |
In this study, we introduce two novel features: the consecutive sequential request ratio and standard deviation of page request depth, for improving the accuracy of malicious and non-malicious web crawler classification from static web server access logs with traditional data mining classifiers. In the first experiment we evaluate the new features on the classification of known well-behaved web crawlers and human visitors. In the second experiment we evaluate the new features on the classification of malicious web crawlers, unknown visitors, well-behaved crawlers and human visitors. The classification performance is evaluated in terms of classification accuracy, and F1 score. The experimental results demonstrate the potential of the two new features to improve the accuracy of data mining classifiers in identifying malicious and well-behaved web crawler sessions.