Improving network security using genetic algorithm approach
Computers and Electrical Engineering
Computer defense using artificial intelligence
SpringSim '07 Proceedings of the 2007 spring simulation multiconference - Volume 3
Price information evaluation and prediction for broiler using adapted case-based reasoning approach
Expert Systems with Applications: An International Journal
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Towards security testing with taint analysis and genetic algorithms
Proceedings of the 2010 ICSE Workshop on Software Engineering for Secure Systems
A case-based classifier for hypertension detection
Knowledge-Based Systems
802.11 de-authentication attack detection using genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Computational intelligence for network intrusion detection: recent contributions
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Automatic network intrusion detection: Current techniques and open issues
Computers and Electrical Engineering
Review: A survey of intrusion detection techniques in Cloud
Journal of Network and Computer Applications
Information and Software Technology
Hi-index | 0.00 |
With the rapid expansion of Internet in recent years, computer systems are facing increased number of security threats. Despite numerous technological innovations for information assurance, it is still very difficult to protect computer systems. Therefore, unwanted intrusions take place when the actual software systems are running. Different soft computing based approaches have been proposed to detect computer network attacks. This paper presents a genetic algorithm (GA) based approach to network intrusion detection, and the software implementation of the approach. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method is easier to implement while providing the flexibility to either generally detect network intrusions or precisely classify the types of attacks. Experimental results show the achievement of acceptable detection rates based on benchmark DARPA data sets on intrusions, while no other complementary techniques or relevant heuristics are applied.