Intrusion detection using a fuzzy genetics-based learning algorithm

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.