A Neural Network based system for Intrusion Detection and attack classification

Anomaly based Intrusion Detection Systems (IDSs) are known to achieve high accuracy and detection rate. However, a significant computational overhead is incurred in training and deploying them. In this paper, we aim to address this issue by proposing a simple Artificial Neural Network (ANN) based IDS model. The proposed IDS model uses the feed forward and the back propagation algorithms along with various other optimization techniques to minimize the overall computational overhead, while at the same time maintain a high performance level. Experimental results on the benchmark NSL-KDD dataset shows that the performance (accuracy and detection rate) of the proposed ANN based IDS model is at par and in some cases even better than other IDS models. Owing to its high performance and low computational overhead, the proposed ANN based IDS model is a suitable candidate for real time deployment and intrusion detection analysis.