TinyIDS - An IoT Intrusion Detection System by Tiny Machine Learning
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65223-3_5
Reference20 articles.
1. Akram, J., Anaissi, A., Othman, W., Alabdulatif, A., Akram, A.: Dronessl: self-supervised multimodal anomaly detection in internet of drone things. IEEE Trans. Consum. Electron. 70, 4287–4298 (2024)
2. Pietrantuono, R., Ficco, M., Palmieri, F.: Survivability analysis of IoT systems under resource exhausting attacks. IEEE Trans. Inf. Forensics Secur. 18, 3277–3288 (2023)
3. Gerodimos, A., Maglaras, L., Ferrag, M.A., Ayres, N., Kantzavelou, I.: IoT: communication protocols and security threats. Internet Things Cyber-Phys. Syst. 3, 1–13 (2023)
4. Pietrantuono, R., Ficco, M., Palmieri, F.: Testing the resilience of MEC-based IoT applications against resource exhaustion attacks. IEEE Trans. Depend. Secure Comput. 21(2), 804–818 (2024)
5. Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., Hafid, A.S.: A comprehensive survey on tinyml. IEEE Access 11, 96892–96922 (2023)
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