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.