A machine learning approach for anomaly detection using genetic algorithm

Malicious intrusions (hacking) into computer systems caught the international interest during the recent years. Network administrators are looking for new ways to protect their resources from hackers. There is a strong need for novel strategies for infrastructure protection. The present available techniques are versatile towards misuse detection and difficult to detect the anomalies. Researchers used neural network models, decision trees, statistical models, and rule-based systems with limited success in detecting anomalies. In recent years, research was diverted towards the application of data mining models [10-13, 18] to intrusion detection. More explorations are continuing for new paradigms and programming techniques. Application of genetic algorithm (GA) models is one of the recent explorations. Researchers have better hope with genetic algorithms [7. 17] and bioinformatics [8] applications. In this paper, we select the key attributes from audit data and presented in patterns to compute inductively learned classifiers that,an recognize anomalies and known intrusions. We used the Bucket Bridge algorithm of the genetics based machine learning to identify the anomalies. Simulation results were presented to detect the anomalies.