Machine learning approach to detect intruders in database based on hexplet data structure

Most of valuable information resources for any organization are stored in the database; it is a serious subject to protect this information against intruders. However, conventional security mechanisms are not designed to detect anomalous actions of database users. An intrusion detection system (IDS), delivers an extra layer of security that cannot be guaranteed by built-in security tools, is the ideal solution to defend databases from intruders. This paper suggests an anomaly detection approach that summarizes the raw transactional SQL queries into a compact data structure called hexplet, which can model normal database access behavior (abstract the user's profile) and recognize impostors specifically tailored for role-based access control (RBAC) database system. This hexplet lets us to preserve the correlation among SQL statements in the same transaction by exploiting the information in the transaction-log entry with the aim to improve detection accuracy specially those inside the organization and behave strange behavior. The model utilizes naive Bayes classifier (NBC) as the simplest supervised learning technique for creating profiles and evaluating the legitimacy of a transaction. Experimental results show the performance of the proposed model in the term of detection rate.