Anomaly Detection in Database using BAT algorithm

Intrusion detection is a very effective mechanism to deal with threats and challenges in database security. The rapid development of the usage of the Internet, E-commerce and cashless transactions has raised the need for high-security mechanism such as Smart Intrusion detection system as traditional detection system not always handle and compete with newly intelligent attacks. Thus, the application of machine learning/ data mining comes to play to identify such unusual attacks in an efficient manner. In this paper, we propose a database intrusion detection system based on the concept of outlier detection which is a derived concept of data mining. We deploy Bat algorithm, a swarm intelligence technique in order to build the intrusion detection model. The performance of the approach is then measured by feeding with an biometric dataset and achieved promising results.