Bro: a system for detecting network intruders in real-time
Computer Networks: The International Journal of Computer and Telecommunications Networking
Machine Learning
Snort - Lightweight Intrusion Detection for Networks
LISA '99 Proceedings of the 13th USENIX conference on System administration
An empirical evaluation of supervised learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Network-Based Anomaly Intrusion Detection Improvement by Bayesian Network and Indirect Relation
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
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ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
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ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Time Series Clustering for Anomaly Detection Using Competitive Neural Networks
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Intrusion Detection Based on Back-Propagation Neural Network and Feature Selection Mechanism
FGIT '09 Proceedings of the 1st International Conference on Future Generation Information Technology
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
TCM-KNN algorithm for supervised network intrusion detection
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
An anomaly intrusion detection approach using cellular neural networks
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Neural network techniques for host anomaly intrusion detection using fixed pattern transformation
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
Building lightweight intrusion detection system based on random forest
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Intrusion detection using PCASOM neural networks
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Evolutionary neural networks for anomaly detection based on the behavior of a program
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A distributed hebb neural network for network anomaly detection
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
CSS'12 Proceedings of the 4th international conference on Cyberspace Safety and Security
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Neural Networks such as RBFN and BPNN have been widely studied in the area of network intrusion detection, with the purpose of detecting a variety of network anomalies (e.g., worms, malware). In real-world applications, however, the performance of these neural networks is dynamic regarding the use of different datasets. One of the reasons is that there are some redundant features for the dataset. To mitigate this issue, in this paper, we propose an approach of combining Neural Networks with Random Forest to improve the accuracy of detecting network intrusions. In particular, we design an intelligent anomaly detection system that uses the algorithm of Random Forest in the process of feature selection and selects an appropriate algorithm in an adaptive way. In the evaluation, we conducted two major experiments using the KDD1999 dataset and a real dataset respectively. The experimental results indicate that Random Forest can enhance the performance of Neural Networks by identifying important and closely related features and that our developed system can select a better algorithm intelligently.