Employment of neural network based classifier for intrusion detection

• Autorzy: Vaitsekhovich L. , Golovko V.
• Języki publikacji: EN

Abstrakty

EN

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 a 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. They are based on the 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 with the help of 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.

Słowa kluczowe

EN

intrusion detection systems (IDS); neural networks

PL

systemy wykrywania włamań; sieci neuronowe

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2008
• Tom: Vol. 2, no. 4
• Strony: 93--98
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Vaitsekhovich L.

autor

Golovko V.

• Brest State Technical University, Moskovskaja str. 267, 224017 Brest, Belarus,

Bibliografia

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