Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure

Author:

El Mrabet Zakaria1,Ezzari Mehdi1,Elghazi Hassan1,El Majd Badr Abou2

Affiliation:

1. National Institute of Posts and Telecommunication, Rabat, Morocco

2. Mohammed V University, Rabat, Morocco

Publisher

ACM Press

Reference28 articles.

1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G. and Isard, M. 2016. Tensorflow: a system for large-scale machine learning. OSDI (2016), 265--283.

2. Al-Dalky, R., Abduljaleel, O., Salah, K., Otrok, H. and Al-Qutayri, M. 2014. A Modbus traffic generator for evaluating the security of SCADA systems. 2014 9th International Symposium on Communication Systems, Networks Digital Sign (CSNDSP) (Jul. 2014), 809--814.

3. Berthier, R., Sanders, W.H. and Khurana, H. 2010. Intrusion Detection for Advanced Metering Infrastructures: Requirements and Architectural Directions. (Oct. 2010), 350--355.

4. Buduma, N. and Locascio, N. 2017. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms. O'Reilly Media, Inc.

5. Chae, H., Jo, B., Choi, S.-H. and Park, T. 2013. Feature selection for intrusion detection using NSL-KDD. Recent advances in computer science. (2013), 184--187.

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