A Network Intrusion Detection System Architecture Based on Snort and Computational Intelligence

DOI

10.2991/icence-16.2016.143How to use a DOI?

Keywords

Network Intrusion Detection, Computational Intelligence, Artificial Neural Network, Snort, Anomaly detection, Misuse detection.

Abstract

With the rapid development of network technology, network attack tools are becoming more and more specialized, hence network intrusion detection becoming greatly difficult, and computational intelligence with its unique advantages in intrusion detection plays more and more important role. On the basis of the detailed analysis of the characteristics of misuse and anomaly detection technology, this paper proposed a network intrusion detection system model based on snort and computational intelligence, which mainly improved the abnormal detection module based on BP neural network, and the KDDCUP99 data set is used to train the BP neural network to carry out the test. The experimental result shows superior performance in terms of both real-time and detection rate via identifying malicious behavior in high speed network traffic.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tao Liu
AU  - Da Zhang
PY  - 2016/09
DA  - 2016/09
TI  - A Network Intrusion Detection System Architecture Based on Snort and Computational Intelligence
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 769
EP  - 775
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.143
DO  - 10.2991/icence-16.2016.143
ID  - Liu2016/09
ER  -