Abstract
As a main method in database intrusion detection, database anomaly detection should be able to detect users’ operational behaviours for timely prevention of possible attacks and for guarantee of database security. Aiming at this, we apply cluster analysis techniques to anomaly detection and propose a novel density-based clustering algorithm called DBCAPSIC, which is adopted to clustering database users according to their behavior types and behavior frequencies. Privilege patterns are extracted from the clusters and serve as a reference in anomaly detection. The simulation experiment proves that the algorithm can recognize the anomalous operations with few mistakes.
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References
Anderson, J.P.: Computer security threat monitoring and surveillance. Technical report, James P. Anderson Company, Fort Washington, Pennsylvania (1980)
Lee, W., Stolfo, S.J.: Data mining approaches for intrusion detection. In: Usenix Security (1998)
Denning, D.E.: An intrusion-detection model. IEEE Transactions on Software Engineering 2, 222–232 (1987)
Sherif, J.S., Dearmond, T.G.: Intrusion detection: systems and models. In: 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 115–115. IEEE Computer Society (2002)
Eskin, E., Miller, M., Zhong, Z.D., et al.: Adaptive model generation for intrusion detection systems (2000)
Ashoor, A.S., Gore, S.: Intrusion detection system (IDS): case study. In: Proceedings of 2011 International Conference on Advanced Materials Engineering (ICAME 2011) (2011)
Kokane, S., Jadhav, A., Mandhare, N., et al.: Intrusion Detection in RBAC Model
Zhang, J., Chen, X.: Research on Intrusion Detection of Database based on Rough Set. Physics Procedia 25, 1637–1641 (2012)
Zhang, Y., Ye, X., Xie, F., et al.: A practical database intrusion detection system framework. In: Ninth IEEE International Conference on Computer and Information Technology, CIT 2009, vol. 1, pp. 342–347. IEEE (2009)
Pang-Ning, T., Steinbach, M., Kumar, V.: Introduction to data mining. Library of Congress (2006)
Campos, M.M., Milenova, B.L.: Creation and deployment of data mining-based intrusion detection systems in oracle database l0g. In: Proceedings of the Fourth International Conference on Machine Learning and Applications, 2005, p. 8. IEEE (2005)
Bloedorn, E., Christiansen, A.D., Hill, W., et al.: Data mining for network intrusion detection: How to get started. MITRE Technical Report (2001)
Feng, W., Zhang, Q., Hu, G., et al.: Mining network data for intrusion detection through combining SVMs with ant colony networks. Future Generation Computer Systems 37, 127–140 (2014)
Kim, M.Y., Lee, D.H.: Data-mining based SQL injection attack detection using internal query trees. Expert Systems with Applications 41(11), 5416–5430 (2014)
Pietraszek, T., Tanner, A.: Data mining and machine learning—towards reducing false positives in intrusion detection. Information Security Technical Report 10(3), 169–183 (2005)
Khan, S.S., Ahmad, A.: Cluster center initialization algorithm for K-means clustering. Pattern Recognition Letters 25(11), 1293–1302 (2004)
Mitra, P., Murthy, C.A., Pal, S.K.: Density-based multiscale data condensation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(6), 734–747 (2002)
Macqueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 1967: Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981)
Brossette, S.E., Ahymel, P.: Data mining and infection control. Clinics in Laboratory Medicine 28(1) (2008)
Giudici, P.: Applied Data Mining: Statistical Methods for Business and Industry. Journal of the American Statistical Association 38(475), 1317–1318 (2006)
Luan, J.: Data Mining and Knowledge Management in Higher Education -Potential Applications. Cluster Analysis (2002)
Zou, B., Ma, X., Kemme, B., Newton, G., Precup, D.: Data mining using relational database management systems. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 657–667. Springer, Heidelberg (2006)
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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Geng, J., Ye, D., Luo, P., Lv, P. (2015). A Novel Clustering Algorithm for Database Anomaly Detection. In: Thuraisingham, B., Wang, X., Yegneswaran, V. (eds) Security and Privacy in Communication Networks. SecureComm 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-319-28865-9_45
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DOI: https://doi.org/10.1007/978-3-319-28865-9_45
Publisher Name: Springer, Cham
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