Application of artificial neural network in detection of DOS attacks

Authors:
Iftikhar Ahmad;Azween B. Abdullah;Abdullah S. Alghamdi
Affiliations:
Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Malaysia;Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Malaysia;College of Computer & Information Sciences, King Saud University, Riyadh, Saudi Arabia
Venue:
Proceedings of the 2nd international conference on Security of information and networks
Year:
2009

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Abstract

A solo attack may cause a big loss in computer and network systems, its prevention is, therefore, very inevitable. Precise detection is very important to prevent such losses. Such detection is a pivotal part of any security tools like intrusion detection system, intrusion prevention system, and firewalls etc. Therefore, an approach is provided in this paper to analyze denial of service attack by using a supervised neural network. The methodology used sampled data from Kddcup99 dataset, an attack database that is a standard for judgment of attack detection tools. The system uses multiple layered perceptron architecture and resilient backpropagation for its training and testing. The developed system is then applied to denial of service attacks. Moreover, its performance is also compared to other neural network approaches which results more accuracy and precision in detection rate.