A Novel Approach for Providing Security for IoT Applications Using Machine Learning and Deep Learning Techniques
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-0011-2_14
Reference20 articles.
1. Al-Garadi, M. A., Mohamed, A., Al-Ali, A., Du, X., Ali, I., Guizani, M.: A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun. Surv. Tutor., pp. 1–46 (2020)
2. Roldán, J., Boubeta-Puig, J., Luis Martínez, J., Ortiz, G.: Integrating complex event processing and machine learning: an intelligent architecture for detecting IoT security attacks. Expert Syst. Appl., pp. 1–22 (2020)
3. Shafique, M., Theocharides, T., Bouganis, C.-S., Hanif, M.A., Khalid, F., Hafiz, R., Rehman, S.: An overview of next-generation architectures for machine learning: roadmap, opportunities and challenges in the IoT era. 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 827–832 (2018)
4. Amanullah, M.A., Habeeb, R.A.A., Nasaruddin, F.H., Gani, A., Ahmed, E., Nainar, A.S.M., Akim, N.M., Imran, M.: Deep learning and big data technologies for IoT security. Comput. Commun., pp. 495–517 (2020)
5. Sagduyu, Y.E., Shi, Y., Erpek, T.: IoT network security from the perspective of adversarial deep learning. In: 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–23 (2019)
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