Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.
Security is becoming a critical part of organizational information systems and security of a computer system or network is compromised when an intrusion takes place. In the field of computer networks security, the detection of threats or attacks is nowadays a critical problem to solve. Intrusion Detection Systems (IDS) have become a standard component in network security infrastructures and is an essential mechanism to protect computer systems from many attacks. In recent years, intrusion detection using data mining have attracted researchers more and more interests. Different researchers propose a different algorithm in different categories. Classifier construction is another research challenge to build an efficient intrusion detection system. KDDCup 1999 intrusion detection dataset plays a key role in fine tuning intrusion detection system and is most widely used by the researchers working in the field of intrusion detection. This paper presents an overview of intrusion detection, KDDCup'99 dataset and detailed analysis of classification techniques used in intrusion detection.
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