Optimizing false positive in anomaly based intrusion detection using Genetic algorithm

In recent years, with increasing use of internet the computer systems are facing many number of security issues. Intrusion detection system (IDS) is one of the principal components of any information security system. Identification of anomalous activity in computer network is first step in identifying the threat to information system. Our focus is mainly on Genetic algorithm (GA) based anomaly detection technique, as GA is one of the most effective evolutionary techniques for machine learning. In this paper Genetic algorithm is applied for network intrusion detection. Our approach for optimization specifically focusing on false positive rate. Reduction in false positive rate also improves accuracy and performance. The limitation of other techniques of accuracy, false positive rates has been addressed in this paper. Experimental results show the efficient detection rates based on KDD99cup datasets which is a standard dataset for intrusion detection.