Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Anomaly Detection Enhanced Classification in Computer Intrusion Detection
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks
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
Computational complexity and the genetic algorithm
Computational complexity and the genetic algorithm
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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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Previous approaches for modeling Intrusion Detection System (IDS) have been on twofold: improving detection model(s) in terms of (i) feature selection of audit data through wrapper and filter methods and (ii) parameters optimization of detection model design, based on classification, clustering algorithms, etc. In this paper, we present three approaches to model IDS in the context of feature selection and parameters optimization: First, we present Fusion of Genetic Algorithm (GA) and Support Vector Machines (SVM) (FuGAS), which employs combinations of GA and SVM through genetic operation and it is capable of building an optimal detection model with only selected important features and optimal parameters value. Second, we present Correlation-based Hybrid Feature Selection (CoHyFS), which utilizes a filter method in conjunction of GA for feature selection in order to reduce long training time. Third, we present Simultaneous Intrinsic Model Identification (SIMI), which adopts Random Forest (RF) and shows better intrusion detection rates and feature selection results, along with no additional computational overheads. We show the experimental results and analysis of three approaches on KDD 1999 intrusion detection datasets.