Paper Title
Intrusion Detection Using Trigonometric Functional Link Artificial Neural Networks

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.