NEURAL NETWORK APPROACHES FOR INTRUSION DETECTION AND RECOGNITION

Vladimir Golovko,Leanid Vaitsekhovich

doi:10.47839/ijc.5.3.416

Abstract

Most current Intrusion Detection Systems (IDS) examine all data features to detect intrusion. Also existing intrusion detection approaches have some limitations, namely impossibility to process large number of audit data for real-time operation, low detection and recognition accuracy. To overcome these limitations, we apply modular neural network models to detect and recognize attacks in computer networks. It is based on combination of principal component analysis (PCA) neural networks and multilayer perceptrons (MLP). PCA networks are employed for important data extraction and to reduce high dimensional data vectors. We present two PCA neural networks for feature extraction: linear PCA (LPCA) and nonlinear PCA (NPCA). MLP is employed to detect and recognize attacks using feature-extracted data instead of original data. The proposed approaches are tested using KDD-99 dataset. The experimental results demonstrate that the designed models are promising in terms of accuracy and computational time for real world intrusion detection.

Highlights

INTRODUCTIONThe goal of Intrusion Detection Systems (IDS) is to protect computer networks from attacks
At present time one of the form of world space globalization is cyber space globalization because of increasing number of computers connected to the Internet
We apply modular neural network models to detect and recognize attacks in computer networks. It is based on combination of principal component analysis (PCA) neural networks and multilayer perceptrons (MLP)

Summary

INTRODUCTION

The goal of Intrusion Detection Systems (IDS) is to protect computer networks from attacks. There are examples of anomaly detection models: IDES [4] and EMERALD [5]. The current anomaly detection systems are not adequate for real-time effective intrusion prevention [11]. In our previous paper [14] we proposed four variants of IDS architectures They were based on combination linear PCA neural network (LPCA) and MLP. In this paper we extend our previous work and examine several models: LPCA and MLP, NPCA and MLP, Ensembling Network (EN).

Result of Classification

IDS ARCHITECTURES

RNN NEURAL NETWORKS

ENSEMBLING AND MLP NEURAL NETWORKS

EXPERIMENTAL RESULTS

CONCLUSION