IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Pairwise classification and support vector machines
Advances in kernel methods
Data Mining Methods for Detection of New Malicious Executables
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Multi-Class SLIPPER System for Intrusion Detection
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
A Serial Combination of Anomaly and Misuse IDSes Applied to HTTP Traffic
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
Application of SVM and ANN for intrusion detection
Computers and Operations Research
A Hybrid Network Intrusion Detection Technique Using Random Forests
ARES '06 Proceedings of the First International Conference on Availability, Reliability and Security
A hybrid machine learning approach to network anomaly detection
Information Sciences: an International Journal
Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set
Intelligent Data Analysis
Intrusion detection using fuzzy association rules
Applied Soft Computing
A new maximal-margin spherical-structured multi-class support vector machine
Applied Intelligence
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
A triangle area based nearest neighbors approach to intrusion detection
Pattern Recognition
Review: Intrusion detection by machine learning: A review
Expert Systems with Applications: An International Journal
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Toward credible evaluation of anomaly-based intrusion-detection methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The use of artificial intelligence based techniques for intrusion detection: a review
Artificial Intelligence Review
Alert correlation in collaborative intelligent intrusion detection systems-A survey
Applied Soft Computing
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A hybrid network intrusion detection system using simplified swarm optimization (SSO)
Applied Soft Computing
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Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k驴1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD'99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems.