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Performance Analysis of Multi-level Hybrid Random Forest and Support Vector Machine based on K-means for Feature Reduced Intrusion Detection

Author(s): Dr. Ravi Singh Pippal1, Dharmendra Kumar2
Affiliation: 1RKDF University, Bhopal, INDIA, 2MP Council of Science & Technology, Bhopal, INDIA,
Series: Volume 01, Issue 01
Pages: 15--23
Publication: December, 2018


Abstract

There is drastic increase in needs of networking and data sharing in today's world. Such globalization of increased information technology and development there exists need of network security. Firewalls may provide some level of security but they never alert administrator for upcoming attacks. In order to find such abnormal behavior of network packets there is need of reliable detection system for improvement of efficiency and accuracy. As in today's developing network environment there is threat of new type of attacks daily in the network. So, the network administration system is also needed to be updated regularly for upgradation of security level. One of the network packet monitoring system is Intrusion detection systems (IDS). The proposed model is designed using machine learning approach for detection of malicious activities of the network packets. For that KDD-99 dataset is used. First of all the dataset is normalized for reducing calculation complexity, further features are reduced using co-relation algorithm. The reduced features determine that only efficient features can be used for malicious behavior detection. From result analysis it is seen that while selecting 15 features using co-relation outperforms best. After feature reduction data clustering is performed using k-mean clustering algorithm. By using clustering, small datasets is built that represents the entire original dataset which can expressively reduce the training time of classifiers and improve the efficiency. In final step of proposed algorithm multilevel hybrid classifiers, based on support vector machine, extreme learning machine and random forest, are designed for classification of dataset into five attack categories i.e. DOS, U2R, R2L, Probe and Normal. As compared to some other multilevel classifier work the proposed algorithm proves its efficiency in terms of high accuracy, high detection rate and false alarm rate (FAR).

Keywords

Intrusion Detection, Feature Reduction, Correlation, Particle Swarm Optimization, Genetic Algorithm, Multilevel Classifiers.

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Cite this paper as:
Ravi Singh Pippal and Dharmendra Kumar, "Performance Analysis of Multi-level Hybrid Random Forest and Support Vector Machine based on K-means for Feature Reduced Intrusion Detection", Research Journal of Engineering Technology and Management (RJETM), vol. 01, no. 01, 2018, pp. 15-23.