IoT-Botnet Detection Using Deep Learning Techniques
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8324-7_22
Reference15 articles.
1. Alazab M (2022) A discrete time-varying greywolf IoT botnet detection system. Comput Commun 192:405–416. https://doi.org/10.1016/j.comcom.2022.06.016
2. Alzahrani MY, Bamhdi AM (2022) Hybrid deep-learning model to detect botnet attacks over internet of things environments. Soft Comput 26(16):7721–7735. https://doi.org/10.1007/s00500-022-06750-4
3. Amin M, Al-Obeidat F, Tubaishat A, Shah B, Anwar S, Tanveer TA (2023) Cyber security and beyond : detecting malware and concept drift in AI-based sensor data streams using statistical techniques. Comput Electr Eng 108:108702. https://doi.org/10.1016/j.compeleceng.2023.108702
4. Hasan T, Malik J, Bibi I, Khan WU, Al-Wesabi FN, Dev K, Huang G (2022) Securing industrial internet of things against botnet attacks using hybrid deep learning approach. IEEE Trans Netw Sci Eng 1–1.https://doi.org/10.1109/TNSE.2022.3168533
5. Hosseini F, Gharehchopogh FS, Masdari M (2022) A botnet detection in IoT using a hybrid multi-objective optimization algorithm. N Gener Comput 40(3):809–843. https://doi.org/10.1007/s00354-022-00188-w
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