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Machine Learning
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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Anomaly Detection Enhanced Classification in Computer Intrusion Detection
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
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HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9 - Volume 9
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set
Intelligent Data Analysis
Fusions of GA and SVM for anomaly detection in intrusion detection system
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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Artificial Intelligence in Medicine
NPC '08 Proceedings of the IFIP International Conference on Network and Parallel Computing
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Distributed denial of service attack detection using an ensemble of neural classifier
Computer Communications
Survey and taxonomy of feature selection algorithms in intrusion detection system
Inscrypt'06 Proceedings of the Second SKLOIS conference on Information Security and Cryptology
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MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Toward lightweight intrusion detection system through simultaneous intrinsic model identification
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
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ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Quantitative intrusion intensity assessment for intrusion detection systems
Security and Communication Networks
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Modeling IDS have been focused on improving detection model(s) in terms of (i) detection model design based on classification algorithm, clustering algorithm, and soft computing techniques such as Artificial Neural Networks (ANN), Hidden Markov Model (HMM), Support Vector Machines (SVM), K-means clustering, Fuzzy approaches and so on and (ii) feature selection through wrapper and filter approaches. However these approaches require large overhead due to heavy computations for both feature selection and cross validation method to minimize generalization errors. In addition selected feature set varies according to detection model so that they are inefficient for modeling lightweight IDS. Therefore this paper proposes a new approach to model lightweight Intrusion Detection System (IDS) based on a new feature selection approach named Correlation-based Hybrid Feature Selection (CBHFS) which is able to significantly decrease training and testing times while retaining high detection rates with low false positives rates as well as stable feature selection results. The experimental results on KDD 1999 intrusion detection datasets show the feasibility of our approach to enable one to modeling lightweight IDS.