Biblio

Application programs are a possible source of attacks to databases as attackers might exploit vulnerabilities in a privileged database application. They can perform code injection or code-reuse attack in order to steal sensitive data. However, as such attacks very often result in changes in the program's behavior, program monitoring techniques represent an effective defense to detect on-going attacks. One such technique is monitoring the library/system calls that the application program issues while running. In this paper, we propose AD-PROM, an Anomaly Detection system that aims at protecting relational database systems against malicious/compromised applications PROgraMs aiming at stealing data. AD-PROM tracks calls executed by application programs on data extracted from a database. The system operates in two phases. The first phase statically and dynamically analyzes the behavior of the application in order to build profiles representing the application's normal behavior. AD-PROM analyzes the control and data flow of the application program (i.e., static analysis), and builds a hidden Markov model trained by the program traces (i.e., dynamic analysis). During the second phase, the program execution is monitored in order to detect anomalies that may represent data leakage attempts. We have implemented AD-PROM and carried experimental activities to assess its performance. The results showed that our system is highly accurate in detecting changes in the application programs' behaviors and has very low false positive rates.

According to recent reports, the most common insider threats to systems are unauthorized access to or use of corporate information and exposure of sensitive data. While anomaly detection techniques have proved to be effective in the detection of early signs of data theft, these techniques are not able to detect sophisticated data misuse scenarios in which malicious insiders seek to aggregate knowledge by executing and combining the results of several queries. We thus need techniques that are able to track users' actions across time to detect correlated ones that collectively flag anomalies. In this paper, we propose such techniques for the detection of anomalous accesses to relational databases. Our approach is to monitor users' queries, sequences of queries and sessions of database connection to detect queries that retrieve amounts of data larger than the normal. Our evaluation of the proposed techniques indicates that they are very effective in the detection of anomalies.