The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Deep Learning Models for Cyber Security in IoT Networks
Abstract
In this paper we propose deep learning models for the cyber security in IoT (Internet of Things) networks. IoT network is as a promising technology which connects the living and non-living things around the world. The implementation of IoT is growing fast but the cyber security is still a loophole, so it is susceptible to many cyber-attack and for the success of any network it most important that the network is completely secure, otherwise people could be reluctant to use this technology. DDoS (Distributed Denial of Service) attack has affected many IoT networks in recent past that has resulted in huge losses. We have proposed deep learning models and evaluated those using latest CICIDS2017 datasets for DDoS attack detection which has provided highest accuracy as 97.16% also proposed models are compared with machine learning algorithms. This paper also identifies open research challenges for usage of deep learning algorithm for IoT cyber security.
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Cites
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“For conducting proposed work we have used latest DDoS attack CICIDS2017 dataset[17, 18].”
MethodCNN Features Off-the-Shelf: An Astounding Baseline for Recognition
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DDoS in the IoT: Mirai and Other Botnets
Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017) · Computer · doi:10.1109/mc.2017.201 · Cited by 1,892
Machine Learning and Deep Learning Methods for Cybersecurity
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018) · IEEE Access · doi:10.1109/access.2018.2836950 · Cited by 1,228
Convolutional Neural Network Architectures for Matching Natural Language Sentences
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Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection
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DeepDefense: Identifying DDoS Attack via Deep Learning
Yuan, X., Li, C., & Li, X. (2017) · 2017 IEEE International Conference on Smart Computing (SMARTCOMP) · doi:10.1109/smartcomp.2017.7946998 · Cited by 390
Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing
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Cited by
Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L. F., & Abdulkadir, S. J. (2022) · Electronics · doi:10.3390/electronics11020198 · Cited by 361
FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks
Jia, Y., Zhong, F., Alrawais, A., Gong, B., & Cheng, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2993782 · Cited by 298
Internet of Things: Evolution, Concerns and Security Challenges
Malhotra, P., Singh, Y., Anand, P., Bangotra, D. K., Singh, P. K., & Hong, W.-C. (2021) · Sensors · doi:10.3390/s21051809 · Cited by 260
Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity
Kuzlu, M., Fair, C., & Guler, O. (2021) · Discover Internet of Things · doi:10.1007/s43926-020-00001-4 · Cited by 259
Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach
Hasan, T., Malik, J., Bibi, I., Khan, W. U., Al-Wesabi, F. N., Dev, K., & Huang, G. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3168533 · Cited by 121
Metadata
- Authors
- Roopak, M., Yun Tian, G., & Chambers, J.
- Venue
- 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)
- Publisher
- IEEE
- Published
- 2019-01
- Pages
- 0452-0457
- OpenAlex
- W2921019731
- Cited by
- 361
- References
- 16
- FWCI
- 35.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Roopak, M., Yun Tian, G., & Chambers, J. (2019). Deep Learning Models for Cyber Security in IoT Networks. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). https://doi.org/10.1109/ccwc.2019.8666588
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