Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
The 1999 DARPA off-line intrusion detection evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
ACM Transactions on Information and System Security (TISSEC)
Results of the KDD'99 classifier learning
ACM SIGKDD Explorations Newsletter
Learning to Forget: Continual Prediction with LSTM
Learning to Forget: Continual Prediction with LSTM
Adaptive Neuro-Fuzzy Intrusion Detection Systems
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Modeling intrusion detection system using hybrid intelligent systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set
Intelligent Data Analysis
A detailed analysis of the KDD CUP 99 data set
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
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This paper evaluates the performance of long short-term memory recurrent neural networks (LSTM-RNN) on classifying intrusion detection data. LSTM networks can learn memory and can therefore model data as a time series. LSTM is trained and tested on a processed version of the KDDCup99 dataset. A variety of suitable performance measures are discussed and applied. Our LSTM network structure and parameters are experimentally obtained within a series of experiments presented. Results finally show that LSTM is able to learn all attack classes hidden in the training data. Furthermore we learn that the receiver operating characteristic (ROC) curve and the corresponding area-under-the-curve (AUC) value are well suited for selecting well performing networks.