Applying Fisher's filter to select KDD connections' features and using neural networks to classify and detect attacks

Title:

Applying Fisher's filter to select KDD connections' features and using neural networks to classify and detect attacks

Creator:

Beghdad, Rachid

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:270b9df7-e9c2-4bfd-8853-4b3fdcbc3094
uuid:270b9df7-e9c2-4bfd-8853-4b3fdcbc3094

Subject:

Intrusion detection systems, Misuse detection, Neural networks, Fisher's ANOVA ranking, Knowledge Discovery and Data mining (KDD) dataset, and KDD Feature reduction

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Most of the neural networks-based intrusion detection systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. That is why the purpose of this study is to identify important KDD features which will be used to train a neural network (NN), in order to best classify and detect attacks. Four NNs were studied: Modular, Recurrent, Principal Component Analysis (PCA), and Time-Lag recurrent (TLR) NNs. We investigated the performance of combining the Fisher's filter used as a feature selection technique, with one of the previously cited NNs. Our simulations show that using Fisher's filter improves largely the performance of the four considered NNs in terms of detection rate, attack classification, and computational time.

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:1

Harvested from:

CDK

Metadata only:

false