Use Case Scenario in Federated Learning-Based Intrusion Detection Systems
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3878-0_55
Reference19 articles.
1. Kumar KS, Nair SAH, Roy DG, Rajalingam B, Kumar RS (2021) Security and privacy-aware artificial intrusion detection system using federated machine learning. Comput Electr Eng 96:107440
2. Li Z, Qin Z, Huang K, Yang X, Ye S (2017) Intrusion detection using convolutional neural networks for representation learning. In: Neural information processing: 24th international conference, ICONIP 2017, Proceedings. Part V, Guangzhou, China, 14–18 Nov 2017. Springer, Berlin, pp 858–866
3. Sharma A, Babbar H, Sharma A (2022) TON-IoT: detection of attacks on internet of things in vehicular networks. In: 2022 6th International conference on electronics, communication and aerospace technology. IEEE, pp 539–545
4. Saini PS, Behal S, Bhatia S (2020) Detection of DDoS attacks using machine learning algorithms. In: 2020 7th International conference on computing for sustainable global development (INDIACom). IEEE, pp 16–21
5. Saba T, Rehman A, Sadad T, Kolivand H, Bahaj SA (2022) Anomaly-based intrusion detection system for IoT networks through deep learning model. Comput Electr Eng 99:107810
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