DL-SkLSTM approach for cyber security threats detection in 5G enabled IIoT-Reference-Cited by-同舟云学术

DL-SkLSTM approach for cyber security threats detection in 5G enabled IIoT

Published:2023-12-18 Issue:1 Volume:16 Page:13-20
ISSN:2511-2104
Container-title:International Journal of Information Technology
language:en
Short-container-title:Int. j. inf. tecnol.

Author:

Rajak Anjali^ORCID,Tripathi Rakesh

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s41870-023-01651-7.pdf

Reference22 articles.

1. Ahmad M, Ahmed I, Jeon G (2021) An IoT-enabled real-time overhead view person detection system based on Cascade-RCNN and transfer learning. J Real Time Image Proc 18:1129–1139

2. Moustafa N et al (2020) Federated TON_IoT Windows datasets for evaluating AI-based security applications. In: 2020 IEEE 19th international conference on trust, security, and privacy in computing and communications (TrustCom). IEEE

3. The internet of things 2020: here's what over 400 IoT decision-makers say about the future of enterprise connectivity and how iot companies can use it to grow revenue. https://www.businessinsider.com/internet-of-thingsreport?IR=T, https://www.fortunebusinessinsights.com/industry-reports/internet-of-things-iot-market-100307. Accessed 3 Jan 2022

4. Ahmed I et al (2022) A multilayer deep learning approach for malware classification in 5G-enabled IIoT. IEEE Trans Ind Inform 19(2):1495–1503

5. Karthic S, Manoj Kumar S, Senthil Prakash PN (2022) Grey wolf-based feature reduction for intrusion detection in WSN using LSTM. Int J Inf Technol 14(7):3719–3724