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Deep learning approach for Network Intrusion Detection in Software Defined Networking
Abstract
Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.
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Cited by
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
Yin, C., Zhu, Y., Fei, J., & He, X. (2017) · IEEE Access · doi:10.1109/access.2017.2762418 · Cited by 2,100
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey
Mao, Q., Hu, F., & Hao, Q. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2846401 · Cited by 789
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2866942 · Cited by 700
Metadata
- Authors
- Tang, T. A., Mhamdi, L., McLernon, D., Zaidi, S. A. R., & Ghogho, M.
- Venue
- 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM)
- Publisher
- IEEE
- Published
- 2016-10
- Pages
- 258-263
- OpenAlex
- W2560162835
- Cited by
- 806
- References
- 0
- FWCI
- 51.85
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Deep learning approach for Network Intrusion Detection in Software Defined Networking. https://app.cochat.ai/papers/10.1109/wincom.2016.7777224
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- Tang, T. A., Mhamdi, L., McLernon, D., Zaidi, S. A. R., & Ghogho, M. (2016). Deep learning approach for Network Intrusion Detection in Software Defined Networking. 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM). https://doi.org/10.1109/wincom.2016.7777224
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