Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Machine Learning
A Real-Time Intrusion Detection System Based on Learning Program Behavior
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
Unsupervised learning techniques for an intrusion detection system
Proceedings of the 2004 ACM symposium on Applied computing
Comparison of BPL and RBF network in intrusion detection system
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Application of neural networks in network control and information security
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Intrusion detection: introduction to intrusion detection and security information management
Foundations of Security Analysis and Design III
An adaptive network intrusion detection method based on PCA and support vector machines
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Security alert correlation using growing neural gas
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
Towards a multiagent-based distributed intrusion detection system using data mining approaches
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
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The application of techniques based on Artificial Intelligence for intrusion detection systems (IDS), mostly, artificial neural networks (ANN), is becoming a mainstream as well as an extremely effective approach to address some of the current problems in this area. Nevertheless, the selection criteria of the features to be used as inputs for the ANNs remains a problematic issue, which can be put, in a nutshell, as follows: The wider the detection spectrum of selected features is, the lower the performance efficiency of the process becomes and vice versa. This paper proposes sort of a compromise between both ends of the scale: a model based on Principal Component Analysis (PCA) as the chosen algorithm for reducing characteristics in order to maintain the efficiency without hindering the capacity of detection. PCA uses a data model to diminish the size of ANN's input vectors, ensuring a minimum loss of information, and consequently reducing the complexity of the neural classifier as well as maintaining stability in training times. A test scenario for validation purposes was developed, using based-on-ANN IDS. The results obtained based on the tests have demonstrated the validity of the proposal.