Application of Artificial Neural Network in Detection of Probing Attacks

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