INTRUSION DETECTION IN DYNAMIC DISTRIBUTED NETWORK USING MACHINE LEARNING BASED ALGORITHMS

Abstract: Intrusion detection is a significant focus of research in the security of computer networks. This paper presents an analysis of the progress being made in the development of effective intrusion detection. Network security becomes more complex due to the changing environment of network and new type of attacks. So it is necessary to design dynamic system to detect new type of attacks. In this paper we define the solution to frequently changing network environment and new types of attacks. The designed system contains two models, Local model and Global model. In the local model, online Gaussian mixture models (GMMs) and online Adaboost processes are used as weak classifiers. A global detection model is constructed by combining the local parametric model. This combination is achieved by using an algorithm based on particle swarm optimization (PSO) and support vector machines (SVM). This system is able to detect new types of attacks. It gives high detection rate and low false alarm rate