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Data-intensive applications exhibit increasing reliance on DatabaseManagement Systems (DBMSs, for short). With the growing cyber-security threatsto government and commercial infrastructures, the need to develop highresilient cyber systems is becoming increasingly important. Cyber-attacks onDBMSs include intrusion attacks that may result in severe degradation inperformance. Several efforts have been directed towards designing an integratedmanagement system to detect, respond, and recover from malicious attacks. Inthis paper, we propose a data Partitioning-based Intrusion Management System(PIMS, for short) that can endure intense malicious intrusion attacks on DBMS.The novelty in PIMS is the ability to contain the damage into data partitions,termed Intrusion Boundaries (IBs, for short). The IB Demarcation Problem (IBDP,for short) is formulated as a mixed integer nonlinear programming. We provethat IBDP is NP-hard. Accordingly, two heuristic solutions for IBDP areintroduced. The proposed architecture for PIMS includes novel IB-centricresponse and recovery mechanisms, which executes compensating transactions.PIMS is prototyped within PostgreSQL, an open-source DBMS. Finally, empiricaland experimental performance evaluation of PIMS are conducted to demonstratethat intelligent partitioning of data tuples improves the overall availabilityof the DBMS under intrusion attacks.

Design and Evaluation of A Data Partitioning-Based Intrusion Management Architecture for Database Systems