Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Automated discovery of concise predictive rules for intrusion detection
Journal of Systems and Software
Evolving computer intrusion scripts for vulnerability assessment and log analysis
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
D-SCIDS: distributed soft computing intrusion detection system
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
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
A hybrid machine learning approach to network anomaly detection
Information Sciences: an International Journal
Improving network security using genetic algorithm approach
Computers and Electrical Engineering
A parallel genetic local search algorithm for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
Intrusion detection techniques and approaches
Computer Communications
Trust based traffic monitoring approach for preventing denial of service attacks
Proceedings of the 2nd international conference on Security of information and networks
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
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Computer networks have expanded significantly in use and numbers. This expansion makes them more vulnerable to attack by unwanted agents. Many current intrusion detection systems (IDS) are unable to identify unknown or mutated attack modes or are unable to operate in a dynamic environment as is necessary with mobile networks. As a result, it is necessary to find new ways to implement and operate intrusion detection systems. Genetic-based systems offer to ability to adapt to changing environments, robustness to noise and the ability to identify unknown attack methods. This paper presents a fuzzy-genetic approach to intrusion detection that is shown to increase the performance of an IDS.