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Threat analysis of IoT networks using artificial neural network intrusion detection system
Open accessAbstract
The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.
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Cited by
Network Intrusion Detection for IoT Security Based on Learning Techniques
Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2896380 · Cited by 940
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3090430 · Cited by 892
CNN-Based Network Intrusion Detection against Denial-of-Service Attacks
Kim, J., Kim, J., Kim, H., Shim, M., & Choi, E. (2020) · Electronics · doi:10.3390/electronics9060916 · Cited by 420
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
Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network
Zhang, Y., Li, P., & Wang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2903723 · Cited by 359
Metadata
- Authors
- Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.-L., Iorkyase, E., Tachtatzis, C., & Atkinson, R.
- Venue
- 2016 International Symposium on Networks, Computers and Communications (ISNCC)
- Publisher
- IEEE
- Published
- 2016-05
- Pages
- 1-6
- OpenAlex
- W2517975492
- Cited by
- 441
- References
- 0
- FWCI
- 20.91
- Type
- conference paper
- Language
- en
- Open access
- green
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- Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.-L., Iorkyase, E., Tachtatzis, C., & Atkinson, R. (2016). Threat analysis of IoT networks using artificial neural network intrusion detection system. 2016 International Symposium on Networks, Computers and Communications (ISNCC). https://doi.org/10.1109/isncc.2016.7746067
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