Attack Detection Availing Feature Discretion using Random Forest Classifier

The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these areas. Machine learning techniques have been successfully used in these defense mechanisms especially IDSs. Although they are effective to some extent in identifying new patterns and variants of existing malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based intrusion detection system based on an ensemble based machine learning classifier called Random Forest with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32 features were identified as significant using feature discretion. Our observations confirm the conjecture that both the feature selection and stochastic based genetic operators improves the accuracy and the effectiveness. The training time is shown to be reduced tremendously by 98.59% and accuracy improved to 98.75%.