i-manager Publications

Support Vector Machine and Principal Component Analysis for Intrusion Detection System

Amir Mukeri*, D P Gaikwad **

*-**Department of Computer Engineering, AISSMS College of Engineering, Pune Maharashtra, India.

Periodicity:January - March'2020
DOI : https://doi.org/10.26634/jse.14.3.17592

Abstract

In recent electronic era, computer networks are substantially evolved because of the rapid development in electronic communication, Internet of Things and Cyber Physical system. In electronic communication technologies, large amount of data is exchanged. As a result, these technologies are prone to several electronic attack, malicious actions, many security threats which can compromise the integrity and availability of information. To overcome these issues, an intrusion detection system is of significant importance in computer network. It is used for security and protection of the various communication infrastructures. For evaluating the performance of various intrusion detection systems, a suitable technique needs to be identified for the application specific dataset. It is very important to study the features of chosen dataset to increase accuracy and decrease training of intrusion detection model. Many researchers use different approaches of feature selection such as principal component analysis, hybrid techniques and chi square methods to decrease training time.

In this paper, an intelligent Network Intrusion Detection is implemented using Support Vector Machine Classifier. NSL KDD dataset is used for training and separate test data to evaluate the performance of the trained model. Different hyper parameter of Support Vector Machine viz. Y and C are used to tuned the model. The performance of this classifier on principal component analysis transformed dataset as well non-transformed dataset is studied and compared. The experimental results show that support vector machine trained on transformed dataset using Principal Component Analysis exhibits 2% less accuracy as compared with classifier trained on non-transferred dataset. However, classifier trained on transformed dataset using Principal Component Analysis take 15% less training time as compared to classifier trained with non-transferred dataset. The lesser accuracy of the Principal Component Analysis transformed data could be interpreted from the explanation of the variance obtained for top Principal Components as they do not capture the linear separation clearly between the two classes.

Keywords

Intrusion Detection System, Support Vector Machine, Feature Selection, Kernel, Parameters.

How to Cite this Article?

Mukeri, A., and Gaikwad, D. P. (2020). Support Vector Machine and Principal Component Analysis for Intrusion Detection System. i-manager's Journal on Software Engineering, 14(3), 42-49. https://doi.org/10.26634/jse.14.3.17592

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Support Vector Machine and Principal Component Analysis for Intrusion Detection System

Abstract

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References

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