ACM Transactions on Information and System Security (TISSEC)
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A hybrid machine learning approach to network anomaly detection
Information Sciences: an International Journal
Conversion methods for symbolic features: A comparison applied to an intrusion detection problem
Expert Systems with Applications: An International Journal
Review: Intrusion detection by machine learning: A review
Expert Systems with Applications: An International Journal
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
The use of artificial intelligence based techniques for intrusion detection: a review
Artificial Intelligence Review
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
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Researchers investigated Artificial Intelligence (AI) based classifiers for intrusion detection to cope the weaknesses of knowledge based systems. AI based classifiers can be utilized in supervised and unsupervised mode. Here, we perform a blind set of experiments to compare & evaluate performance of the supervised classifiers by their categories using variety of metrics. The performance of the classifiers is analyzed using subset of benchmarked KDD cup 1999 dataset as training & Test dataset. This work has significant aspect of using variety of performance metrics to evaluate the supervised classifiers because some classifiers are designed to optimize some specific metric. This empirical analysis is not only a comparison of various classifiers to identify best classifier on the whole and best classifiers for individual attack classes, but also reveals guidelines for researchers to apply AI based classifiers to field of intrusion detection and directions for further research in this field.