Detecting anomalous access patterns in relational databases

VLDB Journal

Volumn 17, Issue 5, 2008, Pages 1063-1077

  Kamra, Ashish ^a   Terzi, Evimaria ^b   Bertino, Elisa ^a  

^a PURDUE UNIVERSITY   (United States)

^b UNIVERSITY OF HELSINKI   (Finland)

Author keywords

Anomaly detection; DBMS; Intrusion detection; RBAC; User profiles

Indexed keywords

EID: 46749133616     PISSN: 10668888     EISSN: 0949877X     Source Type: Journal    
DOI: 10.1007/s00778-007-0051-4     Document Type: Article

References (28)

1. Agrawal, R., Kiernan, J., Srikant, R., Xu, Y.: Hippocratic databases. In Proceedings of the 28th International Conference on Very Large Data Bases (VLDB), pp.143-154. Morgan-Kaufmann, New York (2002)
2. Anton, A., Bertino, E., Li, N., Yu, T.: A roadmap for comprehensive online privacy policies. In: CERIAS Technical Report (2004)
3. Axelsson, S.: Intrusion detection systems: a survey and taxonomy. Technical Report 99-15, Chalmers Univ., (2000)
4. Bertino, E., Kamra, A., Terzi, E.: Intrusion detection in rbac-administered databases. In: Proceedings of the Applied Computer Security Applications Conference (ACSAC) (2005)
5. Bertino, E., Leggieri, T., Terzi, E.: Securing dbms: characterizing and detecting query floods. In: Proceedings of the International Security Conference (ISC) (2004)
6. Chung, C., Gertz, M., Levitt, K.: Demids: a misuse detection system for database systems. In: Integrity and Internal Control in Information Systems: Strategic Views on the Need for Control. IFIP TC11 WG11.5 Third Working Conference (2000)
7. Cooper G.F. (1990). The computational complexity of probabilistic inference using bayesian belief networks. Artif. Intell. 42(2-3): 393-405
8. Domingos P. and Pazzani M.J. (1997). On the optimality of the simple bayesian classifier under zero-one loss. Mach. Learn. 29(2-3): 103-130
9. Friedman N., Geiger D. and Goldszmidt M. (1997). Bayesian network classifiers. Mach. Learn. 29(2-3): 131-163
10. Hilden J. (1984). Statistical diagnosis based on conditional independence does not require it. Comput. Biol. Med. 14(4): 429-435
11. Hochbaum D.S. and Shmoys DB. (1985). A best possible approximation algorithm for the k-center problem. Math. Oper. Res. 10: 180-184
12. Hoglund, K.H.A., Sorvari, A.: A computer host-based user anomaly detection using the self-organizing map. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN) (2000)
13. Hu, Y., Panda, B.: Identification of malicious transactions in database systems. In: Proceedings of the International Database Engineering and Applications Symposium (IDEAS) (2003)
14. Iglewicz B. and Hoaglin D.C. (1993). How to Detect and Handle Outliers. ASQC Quality Press, Milwaukee, Wisconsin
15. Kruegel, C., Vigna, G.: Anomaly detection of web-based attacks. In: Proceedings of the ACM Conference on Computer and Communications Security (CCS) (2003)
16. Lane T. and Brodley CE. (1999). Temporal sequence learning and data reduction for anomaly detection. ACM Trans. Inf. Syst. Secur. (TISSEC) 2(3): 295-331
17. Langley, P., Iba, W., Thompson, K.: An analysis of bayesian classifiers. In: National Conference on Artificial Intelligence pp.223-228 (1992)
18. Lee, S.Y., Low, W.L., Wong, P.Y. Learning fingerprints for a database intrusion detection system. In: ESORICS '02: Proceedings of the 7th European Symposium on Research in Computer Security London. pp. 264-280, Springer-Heidelburg (2002)
19. Lee, V., Stankovic, J., Son, S.: Intrusion detection in real-time databases via time signatures. In: Proceedings of the IEEE Real-Time Technology and Applications Symposium (RTAS) (2000)
20. Liu, P.: Architectures for intrusion tolerant database systems. In: Proceedings of the Annual Computer Security Applications Conference (ACSAC) (2002)
21. Lunt, T., Tamaru, A., Gilham, F., Jagannathan, R., Neumann, P., Javitz, H., Valdes, A., Garvey, T.: A real-time intrusion detection expert system (ides)-final technical report. Technical Report, Computer Science Laboratory, SRI International (1992)
22. Mitchell TM. (1997). Machine Learning. McGraw-Hill, Newyork
23. Sandhu, R., Ferraiolo, D., Kuhn, R.: The nist model for role based access control: Towards a unified standard. In: Proceedings of the 5th ACM Workshop on Role Based Access Control (2000)
24. Spalka, A., Lehnhardt, J.: A comprehensive approach to anomaly detection in relational databases. In: DBSec, pp. 207-221 (2005)
25. Talpade, R., Kim, G., Khurana, S.: Nomad: traffic-based network monitoring framework for anomaly detection. In: Proceedings of the 4th IEEE Symposium on Computers and Communications (ISCC) (1998)
26. Valeur, F., Mutz, D., Vigna, G.: A learning-based approach to the detection of sql attacks. In: Proceedings of the International Conference on detection of intrusions and malware, and vulnerability assessment (DIMVA) (2003)
27. Wenhui, S., Tan, T.: A novel intrusion detection system model for securing web-based database systems. In: Proceedings of the 25th Annual International Computer Software and Applications Conference (COMPSAC) (2001)
28. Yao, Q., An, A., Huang, X. Finding and analyzing database user sessions. In: Proceedings of the 10th International Conference on Database Systems for Advanced Applications (DASFAA) (2005)