A highly secured intrusion detection system for IoT using EXPSO-STFA feature selection for LAANN to detect attacks-Reference-Cited by-同舟云学术

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A highly secured intrusion detection system for IoT using EXPSO-STFA feature selection for LAANN to detect attacks

Published:2022-05-21 Issue:1 Volume:26 Page:559-574
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Jeyaselvi M.,Dhanaraj Rajesh Kumar,Sathya M.,Memon Fida Hussain,Krishnasamy Lalitha,Dev Kapal,Ziyue Wang,Qureshi Nawab Muhammad Faseeh^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

https://link.springer.com/content/pdf/10.1007/s10586-022-03607-1.pdf

Reference31 articles.

1. Balakrishnan, N., Rajendran, A., Pelusi, D., Ponnusamy, V.: Deep belief network enhanced intrusion detection system to prevent security breach in the internet of things. Internet Things 14, 100112 (2019). https://doi.org/10.1016/j.iot.2019.100112

2. Qureshi, A.U.H., Larijani, H., Ahmad, J., Mtetwa, N.: A heuristic intrusion detection system for Internet-of-Things (IoT). In: Intelligent computing-proceedings of the computing conference. Springer, Cham, pp. 86–98 (2019)

3. Eskandari, M., Janjua, M.Z.H., Vecchio, M., Antonelli, F.: Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices. IEEE Internet Things J. 7(8), 6882–6897 (2020)

4. Thamilarasu, G., Chawla, S.: Towards deep-learning-driven intrusion detection for the internet of things. Sensors 19(9), 1977 (2019). https://doi.org/10.3390/s19091977

5. Kumar, V., Das, A.K., Sinha, D.: UIDS: A unified intrusion detection system for IoT environment. Evol. Intell. 14(1), 47–59 (2021). https://doi.org/10.1007/s12065-019-00291-w

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