Experiments on detection of Denial of Service attacks using REPTree

Abstract

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack, exhausts the resources of server/service and makes it unavailable for legitimate users. With increasing use of online services and attacks on these services, the importance of Intrusion Detection System (IDS) for detection of DoS/DDoS attacks has also grown. Different techniques such as data mining, neural network, genetic algorithms, pattern recognition are being used to design IDS. Tree based classifier is one of the widely used data mining technique for design of IDS. This paper evaluates variation in performance of REPTree classifier for intrusion detection when used in combination with different data pre-processing and feature selection methods. Experimental results prove that accuracy of REPTree classifier is improved and performs better than other tree based classifiers when used in combination with feature selection and data pre-processing methods.