Towards mechanisms for detection and prevention of data exfiltration by insiders: keynote talk paper

Data represent an extremely important asset for any organization. Confidential data such as military secrets or intellectual property must never be disclosed outside the organization. Therefore, one of the most severe threats in the case of cyber-insider attacks is the loss of confidential data due to exfiltration. A malicious insider who has the proper credentials to access the organization databases may, over time, send data outside the organization network through a variety of channels, such as email, crafted HTTP requests that encapsulate data, etc. Existing security tools for detection of cyber-attacks focus on protecting the boundary between the organization and the outside world. Numerous network-level intrusion detection systems (IDS) exist, which monitor the traffic pattern and attempt to infer anomalous behavior. While such tools may be effective in protecting against external attacks, they are less suitable when the data exfiltration is performed by an insider who has the proper credentials and authorization to access resources within the organization. In this paper, we argue that DBMS-layer detection and prevention systems are the best alternative to defend against data exfiltration because: (1) DBMS access is performed through a standard, unique language (SQL) with well-understood semantics; (2) monitoring the potential disclosure of confidential data is more effective if done as close as possible to the data source; and (3) the DBMS layer already has in place a thorough mechanism for enforcing access control based on subject credentials. By analyzing the pattern of interaction between subjects and the DBMS, it is possible to detect anomalous activity that is indicative of early signs of exfiltration. In the paper, we outline a taxonomy of cyber-insider dimensions of activities that are indicative of data exfiltration, and we discuss a high-level architecture and mechanisms for early detection of exfiltration by insiders. We also outline a virtualization-based mechanism that prevents insiders from exfiltrating data, even in the case when they manage to gain control over the network. The protection mechanism relies on explicit authorization of data transfers that cross the organizational boundary.