Abstract
The relational database is designed to store and process large amount of information such as business records and personal data. There are many policies and access control techniques for database security, but they are not sufficient for detecting insider attacks. In order to detect threats for the database application, it is necessary to adopt role-based access control (RBAC) and classify the roles according to the authority of each user. In this paper, we propose a method of classifying user’s role and authority using the CNN-LSTM neural networks by extracting features from SQL queries. In the anomaly detection method, CNN automatically extracts important features from database query and LSTM models the temporal information of the SQL sequence. The class activation map also identifies the SQL query features that affect the classification. Experiments with the TPC-E scenario-based benchmark query dataset show that the CNN-LSTM neural networks surpass other state-of-the-art machine learning methods, achieving an overall accuracy of 93.3% and recall of 88.7%. We also identify the characteristics of misclassification data through statistical analysis.
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References
Bertino, E., Sandhu, R.: Database security-concepts, approaches, and challenges. IEEE Trans. Dependable Secure Comput. 1, 2–19 (2005)
Ni, Q., et al.: Privacy-aware role-based access control. ACM Trans. Inf. Syst. Secur. (TISSEC) 13(3), 24–34 (2010)
Li, D., Liu, C., Wei, Q., Liu, Z., Liu, B.: RBAC-based access control for SaaS systems. In: International Conference on Information Engineering and Computer Science (ICIECS), pp. 1–4 (2010)
Liao, H.J., Lin, C.H.R., Lin, Y.C., Tung, K.Y.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16–24 (2013)
Chen, S., et al.: TPC-E vs. TPC-C: Characterizing the new TPC-E benchmark via an I/O comparison study. ACM SIGMOD Rec. 39(3), 5–10 (2011)
Ramachandran, R., Arya, P., Jayanthy, P.G.: A novel method for intrusion detection in relational databases. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 230–235 (2017)
Kumar, G.R., Mangathayaru, N., Narasimha, G.: An improved k-means clustering algorithm for intrusion detection using Gaussian function. In: International Conference on Engineering & MIS, pp. 69–79 (2015)
Horng, S.J., et al.: A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert Syst. Appl. 38(1), 306–313 (2011)
Ronao, C.A., Cho, S.-B.: Anomalous query access detection in RBAC-administered databases with random forest and PCA. Inf. Sci. 369, 238–250 (2016)
Rai, K., Devi, M.S., Guleria, G.: Decision tree based algorithm for intrusion detection. Int. J. Adv. Netw. Appl. 7(4), 2828–2838 (2016)
Mulay, S.A., Devale, P.R., Garje, G.B.: Intrusion detection system using support vector machine and decision tree. Int. J. Comput. Appl. 3(3), 40–43 (2010)
Kim, J.-Y., Cho, S.-B.: Exploiting deep convolutional neural networks for a neural-based learning classifier system. Neurocomputing 354, 61–70 (2019)
Qiu, C., Shan, J., Shandong, B.: Research on intrusion detection algorithm based on BP neural network. Int. J. Secur. Appl. 9(4), 247–258 (2015)
Devaraju, S., Ramakrishnan, S.: Detection of accuracy for intrusion detection system using neural network classifier. Int. J. Emerg. Technol. Adv. Eng. 3(1), 338–345 (2013)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929 (2016)
Kim, T.-Y., Cho, S.-B.: Particle swarm optimization-based CNN-LSTM networks for anomalous query access control in RBAC-administered model. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 123–132. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_11
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This work was supported by the grant funded by 2019 IT promotion fund (Development of AI based Precision Medicine Emergency System) of the Korea government (Ministry of Science and ICT).
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Kim, TY., Cho, SB. (2019). CNN-LSTM Neural Networks for Anomalous Database Intrusion Detection in RBAC-Administered Model. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_15
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DOI: https://doi.org/10.1007/978-3-030-36808-1_15
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