Abstract
In this paper, we have suggested a deep learning model aimed at effective detection of malicious transactions in a database system. This method focuses on exploiting the user normal behavior, data dependencies, and data sensitivity of a transaction to predict intrusions. Currently, we have used different kinds of neural networks according to their strengths of predicting the intrusion according to the type of data such as sequential or featured data. For experimental evaluation, we have used a recurrent neural network for sequence data and feed-forward with back propagation for other attributes, together creating a hybrid deep learning model which works effectively to predict the database intrusions.
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
References
Hu, Y., Panda, B.: A data mining approach for database intrusion detection. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 711–716. ACM (2004)
Peddabachigari, S., Abraham, A., Grosan, C., Thomas, J.: Modeling intrusion detection system using hybrid intelligent systems. J. Netw. Comput. Appl. 30(1), 114–132 (2007)
Ektefa, M., Memar, S., Sidi, F., Affendey, L.S.: Intrusion detection using data mining techniques. In: 2010 International Conference on Information Retrieval & Knowledge Management (CAMP), pp. 200–203. IEEE (2010)
Denatious, D.K., John, A: Survey on data mining techniques to enhance intrusion detection. In: 2012 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–5. IEEE (2012)
Deng, L., Chen, J.: Sequence classification using the high-level features extracted from deep neural networks. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6844–6848. IEEE (2014)
Sahasrabuddhe, A., Naikade, S., Ramaswamy, A., Sadliwala, B., Futane, P.: Survey on Intrusion Detection System using Data Mining Techniques (2017)
Yin, C., Zhu, Y., Fei, J., He, X.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954–21961 (2017)
Panigrahi, S., Sural, S., Majumdar, A.K.: Two-stage database intrusion detection by combining multiple evidence and belief update. Inf. Syst. Front. 15(1), 35–53
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sahu, R.K., Panigrahi, S. (2020). Application of Deep Learning for Database Intrusion Detection. In: Pati, B., Panigrahi, C., Buyya, R., Li, KC. (eds) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1082. Springer, Singapore. https://doi.org/10.1007/978-981-15-1081-6_43
Download citation
DOI: https://doi.org/10.1007/978-981-15-1081-6_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1080-9
Online ISBN: 978-981-15-1081-6
eBook Packages: EngineeringEngineering (R0)