Training all the KDD data set to classify and detect attacks

Title:

Training all the KDD data set to classify and detect attacks

Creator:

Beghdad, Rachid

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:2d3dda03-6658-44ce-a35e-ebb05e62d1d4
uuid:2d3dda03-6658-44ce-a35e-ebb05e62d1d4

Subject:

Intrusion detection systems, misuse detection, neural networks, attack categories, and KDD data sets

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2007 Volume:17 | Number:2

Harvested from:

CDK

Metadata only:

false