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.
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