Decision tree classifier for network intrusion detection with GA-based feature selection
Proceedings of the 43rd annual Southeast regional conference - Volume 2
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Computer Networks: The International Journal of Computer and Telecommunications Networking
A hybrid machine learning approach to network anomaly detection
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Unsupervised Anomaly Detection Using HDG-Clustering Algorithm
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Support vector regression for link load prediction
Computer Networks: The International Journal of Computer and Telecommunications Networking
Enhancing network based intrusion detection for imbalanced data
International Journal of Knowledge-based and Intelligent Engineering Systems
Modeling Network Intrusion Detection System Using Feature Selection and Parameters Optimization
IEICE - Transactions on Information and Systems
MA-DIDS: A Multi-Agent Based Distributed Intrusion Detection System
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Intrusion Detection Method Using Neural Networks Based on the Reduction of Characteristics
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Features selection for intrusion detection systems based on support vector machines
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Information Sciences: an International Journal
Meta learning intrusion detection in real time network
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Feature selection using rough-DPSO in anomaly intrusion detection
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Data Mining and Knowledge Discovery
Toward credible evaluation of anomaly-based intrusion-detection methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Selection of effective network parameters in attacks for intrusion detection
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A comparison of feature-selection methods for intrusion detection
MMM-ACNS'10 Proceedings of the 5th international conference on Mathematical methods, models and architectures for computer network security
Expert Systems with Applications: An International Journal
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Intelligent Data Analysis
Mutual information-based feature selection for intrusion detection systems
Journal of Network and Computer Applications
Principal component neural networks based intrusion feature extraction and detection using SVM
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Kernel PCA based network intrusion feature extraction and detection using SVM
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Survey and taxonomy of feature selection algorithms in intrusion detection system
Inscrypt'06 Proceedings of the Second SKLOIS conference on Information Security and Cryptology
Toward lightweight detection and visualization for denial of service attacks
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
An anomaly intrusion detection approach using cellular neural networks
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FADS: a fuzzy anomaly detection system
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
A hybrid neural network approach to the classification of novel attacks for intrusion detection
ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
Computational intelligence for network intrusion detection: recent contributions
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
SVM approach with a genetic algorithm for network intrusion detection
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Intrusion detection of DoS/DDoS and probing attacks for web services
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Building lightweight intrusion detection system based on random forest
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
CISC'05 Proceedings of the First SKLOIS conference on Information Security and Cryptology
Intrusion detection based on MLP neural networks and k-means algorithm
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Intelligent hierarchical intrusion detection system for secure wireless ad hoc network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
A fuzzy anomaly detection system
WISI'06 Proceedings of the 2006 international conference on Intelligence and Security Informatics
Automatic network intrusion detection: Current techniques and open issues
Computers and Electrical Engineering
Quantitative intrusion intensity assessment for intrusion detection systems
Security and Communication Networks
ISC'07 Proceedings of the 10th international conference on Information Security
Features selection approaches for intrusion detection systems based on evolution algorithms
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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Intrusion detection is a critical component ofsecure information systems. This paper addresses theissue of identifying important input features in buildingan intrusion detection system (IDS). Since eliminationof the insignificant and/or useless inputs leads to asimplification of the problem, faster and more accuratedetection may result. Feature ranking and selection,therefore, is an important issue in intrusion detection.In this paper we apply the technique of deletingone feature at time to perform experiments on SVMsand neural networks to rank the importance of inputfeatures for the DARPA collected intrusion data.Important features for each of the 5 classes of intrusionpatterns in the DARPA data are identified.It is shown that SVM-based and neural networkbased IDSs using a reduced number of features candeliver enhanced or comparable performance. An IDSfor class-specific detection based on five SVMs isproposed.