Intrusion detection systems for IoT based on bio-inspired and machine learning techniques: a systematic review of the literature

Author:

Saadouni Rafika,Gherbi Chirihane,Aliouat Zibouda,Harbi Yasmine,Khacha Amina

Publisher

Springer Science and Business Media LLC

Reference88 articles.

1. Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., Ahmad, F.: Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommun. Technol. 32(1), e4150 (2021)

2. Khan, I.A., Moustafa, N., Pi, D., Sallam, K.M., Zomaya, A.Y., Li, B.: A new explainable deep learning framework for cyber threat discovery in industrial iot networks. IEEE Internet Things J. 9(13), 11604–11613 (2021)

3. Harbi, Y., Aliouat, Z., Refoufi, A., Harous, S.: Recent security trends in internet of things: a comprehensive survey. IEEE Access 9, 113292–113314 (2021)

4. Gherbi, C., Senouci, O., Harbi, Y., Medani, K., Aliouat, Z.: A systematic literature review of machine learning applications in IoT. Int. J. Commun. Syst. 36(11), e5500 (2023)

5. Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. Appl. 50, 102419 (2020)

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