Hybrid deep-learning model to detect botnet attacks over internet of things environments
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-022-06750-4.pdf
Reference62 articles.
1. Aburomman AA, Reaz MBI (2016) Review of IDS development methods in machine learning. Int J Electr Comput Eng (IJECE) 6:2432–2436
2. Ahmad Z, Khan AS, Shiang CW, Abdullah J, Ahmad F (2021) Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.4150
3. Ahmed AA, Jabbar WA, Sadiq AS, Patel H (2020) Deep learning-based classification model for botnet attack detection J Ambient Intell Humaniz Comput
4. Alkahtani H, Aldhyani THH (2020) Botnet attack detection by using CNN-LSTM model for internet of things applications. Secur Commun Networks 2021:3806459. https://doi.org/10.1155/2021/3806459
5. Al Shorman A, Faris H, Aljarah I (2020) Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection. J Ambient Intell Humaniz Comput 11:2809–2825
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