IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Classifier systems and genetic algorithms
Artificial Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Intrusion Detection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using Artificial Anomalies to Detect Unknown and Known Network Intrusions
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Fuzzy Classifier System That Generates Linguistic Rules for Pattern Classification Problems
Selected papers from the EEE/Nagoya-University World Wisepersons Workshop on Fuzzy Logic, Neural Networks, and Evolutionary Computation
A Neural Network Component for an Intrusion Detection System
SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
USTAT: A Real-Time Intrusion Detection System for UNIX
SP '93 Proceedings of the 1993 IEEE Symposium on Security and Privacy
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Lightweight agents for intrusion detection
Journal of Systems and Software
Intrusion detection using an ensemble of intelligent paradigms
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
A parallel genetic local search algorithm for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
A fuzzy-genetic approach to network intrusion detection
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Journal of Network and Computer Applications
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Design and analysis of genetic fuzzy systems for intrusion detection in computer networks
Expert Systems with Applications: An International Journal
Mutual information-based feature selection for intrusion detection systems
Journal of Network and Computer Applications
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
A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis
Expert Systems with Applications: An International Journal
Policy-enhanced ANFIS model to counter SOAP-related attacks
Knowledge-Based Systems
Optimizing the modified fuzzy ant-miner for efficient medical diagnosis
Applied Intelligence
Fuzzy particle swarm optimization for intrusion detection
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Ensemble fuzzy rule-based classifier design by parallel distributed fuzzy GBML algorithms
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
Fuzzy systems have demonstrated their ability to solve different kinds of problems in various applications domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridize fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems and genetic fuzzy systems hybridize the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The objective of this paper is to describe a fuzzy genetics-based learning algorithm and discuss its usage to detect intrusion in a computer network. Experiments were performed with DARPA data sets [KDD-cup data set. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html], which have information on computer networks, during normal behaviour and intrusive behaviour. This paper presents some results and reports the performance of generated fuzzy rules in detecting intrusion in a computer network.