Database Intrusion Detection Systems (DIDs): Insider Threat Detection via Behavioural-based Anomaly Detection Systems -- A Brief Survey of Concepts and Approaches
It addresses insider threat detection in database security, but is incremental as it reviews existing concepts and approaches.
The paper surveys behavioral-based database intrusion detection systems (DIDs) for detecting insider threats, highlighting their effectiveness in identifying malicious accesses by legitimate users who abuse privileges.
One of the data security and privacy concerns is of insider threats, where legitimate users of the system abuse the access privileges they hold. The insider threat to data security means that an insider steals or leaks sensitive personal information. Database Intrusion detection systems, specifically behavioural-based database intrusion detection systems, have been shown effective in detecting insider attacks. This paper presents background concepts on database intrusion detection systems in the context of detecting insider threats and examines existing approaches in the literature on detecting malicious accesses by an insider to Database Management Systems (DBMS).