Experiments on detection of Denial of Service attacks using Naive Bayesian classifier

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can result in huge loss of data and make resources unavailable for legitimate users. With continuous growth of Internet users and traffic, the importance of Intrusion Detection System (IDS) for detection of DoS/DDoS network attacks has also grown. Different techniques such as data mining and pattern recognition are being used to design IDS. Naïve Bayesian is a widely used classifier for design of IDS. This paper evaluates variation in performance of Naïve Bayesian classifier for intrusion detection when used in combination with different data pre-processing and feature selection methods. Experimental results prove that accuracy of Naïve Bayesian classifier is improved and performs better than other classifiers when used in combination with Feature Selection and data pre-processing methods.