  Data-intensive applications exhibit increasing reliance on Database
Management Systems (DBMSs, for short). With the growing cyber-security threats
to government and commercial infrastructures, the need to develop high
resilient cyber systems is becoming increasingly important. Cyber-attacks on
DBMSs include intrusion attacks that may result in severe degradation in
performance. Several efforts have been directed towards designing an integrated
management system to detect, respond, and recover from malicious attacks. In
this 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 prove
that IBDP is NP-hard. Accordingly, two heuristic solutions for IBDP are
introduced. The proposed architecture for PIMS includes novel IB-centric
response and recovery mechanisms, which executes compensating transactions.
PIMS is prototyped within PostgreSQL, an open-source DBMS. Finally, empirical
and experimental performance evaluation of PIMS are conducted to demonstrate
that intelligent partitioning of data tuples improves the overall availability
of the DBMS under intrusion attacks.
