Strengthening Network Intrusion Detection in IoT Environments with Self-supervised Learning and Few Shot Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70819-0_7
Reference18 articles.
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2. Almomani, I., Al-Kasasbeh, B., Al-Akhras, M., et al.: Wsn-ds: a dataset for intrusion detection systems in wireless sensor networks. J. Sensors 2016 (2016)
3. Atitallah, S.B., Driss, M., Almomani, I.: A novel detection and multi-classification approach for iot-malware using random forest voting of fine-tuning convolutional neural networks. Sensors 22(11), 4302 (2022)
4. Atitallah, S.B., Driss, M., Boulila, W., Ghézala, H.B.: Leveraging deep learning and iot big data analytics to support the smart cities development: Review and future directions. Comput. Sci. Rev. 38, 100303 (2020)
5. Atitallah, S.B., Driss, M., Ghezala, H.B.: Fedmicro-ida: a federated learning and microservices-based framework for iot data analytics. Internet of Things 23, 100845 (2023)
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