Home > Archive > 2012 > Volume 2 Number 5 (Oct. 2012) >
IJMLC 2012 Vol.2(5): 583-587 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.194

Intrusion Detection Using PCA Based Modular Neural Network

Khaled Al-Nafjan , Musaed A. Al-Hussein , Abdullah S. Alghamdi, Mohammad Amanul Haque, and Iftikhar Ahmad

Abstract—Most of current intrusion detection systems are based on machine learning methods but very few till now use clustering algorithms as a preprocessing layer to reduce the high dimensionality of data, which is difficult to analyze. In this paper we introduce Modular Neural Network for intrusion detection, which apply Principal Component Analysis (PCA) as preprocessing layer for reducing huge information quantity presented in knowledge discovery and data mining (KDD99) data set. PCA significantly reduce the high dimensionality of data set without loss of information. Then this preprocess data in the form of principal component is presented to Batch Backpropagation Neural Network for efficient intrusion detection. We rely on some experiments to calculate Root Mean Square Error (RMSE) using Modular Neural Network on KDD 99 data set. Our experimental results show improvement in the learning time due to the reduction of high dimensions of data. Also we have obtained low RMSE during training, which is below the acceptance range of 0.1. Proposed Modular Neural Network has capability to efficiently and accurately classify data into attack and normal.

Index Terms—Intrusion Detection, Principal Component Analysis, Modular Neural Network, KDD99 dataset, Batch Backpropagation Neural Network.

The authors are with Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia (e-mail: ghamdi@ksu.edu.sa; amanulhaque80@gmail.com; kalnafjan@ksu.edu.sa).


Cite:Khaled Al-Nafjan , Musaed A. Al-Hussein , Abdullah S. Alghamdi, Mohammad Amanul Haque, and Iftikhar Ahmad, "Intrusion Detection Using PCA Based Modular Neural Network," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 583-587, 2012.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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