Database Intrusion Detection System Using Octraplet and Machine Learning

Over the years digitization has increased to such an extent that each and every service is being continuously automated and made online. Online services gained immense popularity and trust that every information both personal and private related to a user is stored in databases. This in turn changed the focus of the attackers towards the databases that stores valuable information. Although Security mechanisms exists for host based systems as well as networks, security breaches still occur every day and data are being stolen. Thus focus towards database security becomes a necessity. This Paper proposes fully automated database intrusion detection system that addresses both insider and outsider attacks that can thwart breaches that goes undetected by network or host based intrusion detection systems. Proposed System is a flexible one that can be fine -tuned with increasing complexity and dynamic nature of databases. Our Architecture is an anomaly based detection mechanism that implements Role based Access control(RBAC). A new Data Structure called Octraplet is used for storing the sql queries. This system uses Naive Bayes Classifier which is a supervised Machine Learning method for Detecting anomalous queries. Proposed approach can improve the detection rates as well as performance of the system.