Abstract
This paper is an attempt to introduce a new approach on increasing the security of database systems. Securing databases involves external as well as internal misuse detection and prevention. SQL injection handling and access control mechanism prevents misuse through unauthorized access to the database. This allows only those users to access database contents who are meant to use it. However, if there is an intentional or unintentional misuse by some authorized user, then it becomes very difficult to identify and prevent that misuse then and there only. Such misuse scenarios can be detected later by auditing the transaction log. Therefore the need for a robust query intrusion detection model for database system arises. The model proposed in this paper detects such types of misuses by authorized users and classifies them as legitimate or anomalous by analyzing the nature of queries they fire and tuning itself based on the responses to the alarms raised.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Zhong, Y., Zhu, Z., Qin, X.-L.: A clustering method based on data queries and its application in database intrusion detection. In: ICMLC 2005, Guangzhou, August 18-21, pp. 2096–2101 (2005)
Gopal, R.D., Goes, P.B., Garfinkel, R.S.: Interval Protection of Confidential Information in a Database. INFORMS Journal on Computing 10(3) (1998)
Caulkins, B., Lee, J., Morgan Wang, F.: A Dynamic Data Mining Technique for Intrusion Detection Systems. In: Proceedings of the 43rd annual Southeast regional conference, Kennesaw, Georgia, vol. 2, pp. 2148–2153 (2005)
Mokube, I., Adams, M.: Honeypots Concepts, Approaches, and Challenges. In: Proceedings of the 45th annual southeast regional conference, Winston-Salem, North Carolina, pp. 321–326 (2007)
Pavlou, K., Snodgrass, R.T.: Forensic Analysis of Database Tampering. In: Proceedings of the 2006 ACM SIGMOD International conference on Management of data, Chicago, IL, USA, pp. 109–120 (2006)
Ceri, S., Di Giunta, F., Lanzi, P.L.: Mining Constraint Violations. In: Proceedings of the 43rd annual Southeast regional conference, Kennesaw, Georgia, March 2007, vol. 2, pp. 1–32 (2007)
Hu, Y., Panda, B.: Identification of Malicious Transactions in Database Systems. In: Proceedings of the Seventh International Database Engineering and Applications Symposium (IDEAS 2003), pp. 1–7 (2003)
Yu, Z., Tsai, J.J.P., Weigert, T.: An Automatically Tuning Intrusion Detection System. IEEE Transactions On Systems, Man and Cybernetics, Cybernetics 37(2), 373–384 (2007)
Bertino, E., Terzi, E., Kamra, A., Vakali, A.: Intrusion Detection in RBAC-administered Databases. In: Proceedings of the 21st Annual Computer Security Applications Conference 2005, pp. 170–182. IEEE, Los Alamitos (2005)
Lee, S.Y., Low, W.L., Wong, P.Y.: Learning Fingerprints for a Database Intrusion Detection System. In: Gollmann, D., Karjoth, G., Waidner, M. (eds.) ESORICS 2002. LNCS, vol. 2502, pp. 264–279. Springer, Heidelberg (2002)
Rietta, F.S.: Application Layer Intrusion Detection for SQL Injection. In: ACM SE 2006, Melbourne, Florida, USA, March 10-12, pp. 531–536 (2006)
Li, Z., Dad, A., Zhou, J.: Theoretical Basis for Intrusion Detection. In: Proceedings of the 2005 IEEE, Workshop on Information Assurance and Security United States Military Academy, West Point, NY, pp. 184–192 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gupta, B., Arora, D., Jha, V. (2010). An Anomaly Based Approach for Intrusion Detection by Authorized Users in Database Systems. In: Prasad, S.K., Vin, H.M., Sahni, S., Jaiswal, M.P., Thipakorn, B. (eds) Information Systems, Technology and Management. ICISTM 2010. Communications in Computer and Information Science, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12035-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-12035-0_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12034-3
Online ISBN: 978-3-642-12035-0
eBook Packages: Computer ScienceComputer Science (R0)