Network Intrusion Detection System Using Neural Networks

Jimmy Shun, Heidar Malki

2008 · 111 citations · 2 references

Concepts

TL;DR

Intrusion detection systems (IDS) have been created to predict and thwart current and future attacks. This paper presents a neural network‑based intrusion detection method for internet‑based attacks on a computer network. The method uses feedforward neural networks trained with back‑propagation on DARPA intrusion detection datasets to identify and predict unusual network activities. Experimental results on real data demonstrate promising detection performance of the neural‑network‑based IDS.

Abstract

This paper presents a neural network-based intrusion detection method for the internet-based attacks on a computer network. Intrusion detection systems (IDS) have been created to predict and thwart current and future attacks. Neural networks are used to identify and predict unusual activities in the system. In particular, feedforward neural networks with the back propagation training algorithm were employed in this study. Training and testing data were obtained from the Defense Advanced Research Projects Agency (DARPA) intrusion detection evaluation data sets. The experimental results on real-data showed promising results on detection intrusion systems using neural networks.

References

2