Principle component analysis based intrusion detection system using support vector machine

N. S.K.H. Praneeth, Naveen M. Varma, Roshan Ramakrishna Naik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Whenever an intrusion happens, privacy and security of the system are compromised. In order to detect different types of attacks that happen in a network, Intrusion Detection System (IDS) plays a crucial role in Network security. IDS is designed in order to classify the activities of the system into abnormal and normal. Machine learning based Intrusion Detection is gaining attention in recent years and is able to give better results with greater accuracy and high detection rate on novel attacks. In this paper, performance of different kernels of Support Vector Machine (SVM) are evaluated against Knowledge Discovery in Databases Cup'99(KDD) data set and detection accuracy, detection time are compared. The detection time is reduced by adopting Principal Component Analysis (PCA) which curtails higher dimensional dataset to lower dimensional dataset. The experiments which are conducted in this research shows that Gaussian Radial Basis Function kernel of SVM has higher detection accuracy.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1344-1350
Number of pages7
ISBN (Electronic)9781509007745
DOIs
Publication statusPublished - 05-01-2017
Externally publishedYes
Event1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Bangalore, India
Duration: 20-05-201621-05-2016

Conference

Conference1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016
CountryIndia
CityBangalore
Period20-05-1621-05-16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Electrical and Electronic Engineering

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  • Cite this

    Praneeth, N. S. K. H., Varma, N. M., & Naik, R. R. (2017). Principle component analysis based intrusion detection system using support vector machine. In 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings (pp. 1344-1350). [7808050] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTEICT.2016.7808050