Summary
The purpose of this study is to analyze the performances of some neural networks (NNs) when all the KDD data set is used to train them, in order to classify and detect attacks. Five different types of NNs were tested: Multi-Layer Perceptron (MLP), Self Organization Feature Map (SOFM), Radial Basis Function/Generalized Regression/Probabilistic (RBF/GR/P), Jordan/Elman, and Recurrent NNs. The experiment study is done on the Knowledge Discovery and Data mining (KDD) data sets. We consider two levels of attack granularities depending on whether dealing with four main categories, or only focusing on the normal/attack connection types. Our simulations show that our results are competitive with some other artificial intelligence or data mining intrusion detection systems.
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Extract
Training All the Kdd Data Set to Classify and Detect Attacks
1. Introduction
The constant development of computer technologies is undeniable. The Internet became a public tool for communication. Networking has become a very important part of our society. With this incredible growth of technology we are facing an increased need for more security. Malicious usage, attacks, and sabotage have been on the rise as more and more computers are put into use. Connecting information systems to networks such as the Internet and public telephone systems further magnifies the potential for exposure through a variety of attack channels. In this research we investigate the problem of Intrusion Detection Systems (IDS), one of very important components of computer/ network security. An IDS by itself does not prevent security brakes, but detects malicious use by monitoring unusual activity. This unusual activity is capable of taking an unlimited number of forms [1].Intrusion detection techniques can be mapped into four classes: anomaly detection, specificat...See the full content of this document
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