Paper Title
Intrusion Detection Using Trigonometric Functional Link Artificial Neural Networks
Abstract
In this day and age computer systems work in extremely dynamic & circulated environments which necessitate
the protection mechanisms to avert intentional or unintentional hostility to security policies. Intrusion detection system is one
of very discussed about solutions. In this paper, we presented a new approach known as Intrusion detection, using
Trigonometric Functional Link Artificial Neural Networks that deals with neural network model to detect intrusion. Neural
network methods are flexible in learning various archetypal problems. In this paper, the attribute normalization technique
are used to a given KDD Cup 1999 dataset .Attribute normalization is essential in detection performance .However, many
intrusion detection methods do not normalize attributes before training and detection. The MATLAB software is used to
train and test the dataset and the classification rate is calculated.