NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Towards a taxonomy of intrusion-detection systems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on computer network security
ACM Transactions on Information and System Security (TISSEC)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Winning the KDD99 classification cup: bagged boosting
ACM SIGKDD Explorations Newsletter
KDD-99 classifier learning contest LLSoft's results overview
ACM SIGKDD Explorations Newsletter
The MP13 approach to the KDD'99 classifier learning contest
ACM SIGKDD Explorations Newsletter
Parzen-Window Network Intrusion Detectors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Adaptive Neuro-Fuzzy Intrusion Detection Systems
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Intrusion detection using a fuzzy genetics-based learning algorithm
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set
Intelligent Data Analysis
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
A fuzzy-genetic approach to network intrusion detection
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
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
An efficient network intrusion detection
Computer Communications
Discovery and prevention of attack episodes by frequent episodes mining and finite state machines
Journal of Network and Computer Applications
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
A novel intrusion detection system based on hierarchical clustering and support vector machines
Expert Systems with Applications: An International Journal
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
Journal of Network and Computer Applications
Mutual information-based feature selection for intrusion detection systems
Journal of Network and Computer Applications
PCA for improving the performance of XCSR in classification of high-dimensional problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Alert correlation in collaborative intelligent intrusion detection systems-A survey
Applied Soft Computing
Practical real-time intrusion detection using machine learning approaches
Computer Communications
Ranger intrusion detection system for wireless sensor networks with Sybil attack based on ontology
AIC'10/BEBI'10 Proceedings of the 10th WSEAS international conference on applied informatics and communications, and 3rd WSEAS international conference on Biomedical electronics and biomedical informatics
Computational intelligence algorithms analysis for smart grid cyber security
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
An effective unsupervised network anomaly detection method
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
Analysing 3G radio network performance with fuzzy methods
Neurocomputing
Engineering Applications of Artificial Intelligence
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An intrusion detection system's main goal is to classify activities of a system into two major categories: normal and suspicious (intrusive) activities. Intrusion detection systems usually specify the type of attack or classify activities in some specific groups. The objective of this paper is to incorporate several soft computing techniques into the classifying system to detect and classify intrusions from normal behaviors based on the attack type in a computer network. Among the several soft computing paradigms, neuro-fuzzy networks, fuzzy inference approach and genetic algorithms are investigated in this work. A set of parallel neuro-fuzzy classifiers are used to do an initial classification. The fuzzy inference system would then be based on the outputs of neuro-fuzzy classifiers, making final decision of whether the current activity is normal or intrusive. Finally, in order to attain the best result, genetic algorithm optimizes the structure of our fuzzy decision engine. The experiments and evaluations of the proposed method were performed with the KDD Cup 99 intrusion detection dataset.