Classifying attacks in a network intrusion detection system based on artificial neural networks

Nowadays with the dramatic growth in communication and computer networks, security has become a critical subject for computer systems. A good way to detect the illegal users is to monitoring these user's packets. Different algorithms, methods and applications are created and implemented to solv...

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Bibliographic Details
Published in13th International Conference on Advanced Communication Technology (ICACT2011) pp. 868 - 873
Main Authors Norouzian, M R, Merati, S
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2011
Subjects
Artificial neural networks
Feature extraction
Intrusion detection
Intrusion Detection System
Monitoring
Multilayer Perceptron
Network Security
Neurons
Testing
Training
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ISBN1424488303
9781424488308
ISSN1738-9445

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Summary:Nowadays with the dramatic growth in communication and computer networks, security has become a critical subject for computer systems. A good way to detect the illegal users is to monitoring these user's packets. Different algorithms, methods and applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize in two groups, normal or threat. This paper presents a new approach of intrusion detection system based on neural network. In this paper, we have a Multi Layer Perceptron (MLP) is used for intrusion detection system. The results show that our implemented and designed system detects the attacks and classify them in 6 groups with the approximately 90.78% accuracy with the two hidden layers of neurons in the neural network.
ISBN:1424488303
9781424488308
ISSN:1738-9445