ANOMALY DETECTION USING ARTIFICIAL NEURAL NETWORK

Abstract

In this research, anomaly detection using neural network is introduced. This research aims to experiment with user behaviour as parameters in anomaly intrusion detection using a backpropagation neural network. Here we wanted to see if a neural network is able to classify normal traffic correctly, and detect known and unknown attacks without using a huge amount of training data. For the training and testing of the neural network, we used the DARPA Intrusion Detection Evaluation data sets. In our final experiment, we have got a classification rate of 88 % on known and unknown attacks. Compared with other researches our result is very promising