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Machine Learning Techniques for Intrusion Detection
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Author(s): Tameem Ahmad (Department of Computer Engineering, Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, India), Mohd Asad Anwar (Department of Computer Engineering, Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, India)and Misbahul Haque (Department of Computer Engineering, Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, India)
Copyright: 2020
Pages: 19
Source title:
Handbook of Research on Intrusion Detection Systems
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India)and Srivathsan Srinivasagopalan (AT&T, USA)
DOI: 10.4018/978-1-7998-2242-4.ch003
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Abstract
This chapter proposes a hybrid classifier technique for network Intrusion Detection System by implementing a method that combines Random Forest classification technique with K-Means and Gaussian Mixture clustering algorithms. Random-forest will build patterns of intrusion over a training data in misuse-detection, while anomaly-detection intrusions will be identiðed by the outlier-detection mechanism. The implementation and simulation of the proposed method for various metrics are carried out under varying threshold values. The effectiveness of the proposed method has been carried out for metrics such as precision, recall, accuracy rate, false alarm rate, and detection rate. The various existing algorithms are analyzed extensively. It is observed experimentally that the proposed method gives superior results compared to the existing simpler classifiers as well as existing hybrid classifier techniques. The proposed hybrid classifier technique outperforms other common existing classifiers with an accuracy of 99.84%, false alarm rate as 0.09% and the detection rate as 99.7%.
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