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/).
Cite this article
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 -