XRFLWID: XGBoost and Random Forest-Based Lightweight Intrusion Detection Model for IoT Attack Detection

Author:

Khanday Shahbaz AhmadORCID,Fatima HoorORCID,Rakesh NitinORCID

Publisher

Springer Nature Singapore

Reference21 articles.

1. Hussain F, Abbas SG, Fayyaz UU, Shah GA, Toqeer A, Ali A (2020) Towards a universal features set for IoT botnet attacks detection. In: 2020 IEEE 23rd international multitopic conference (INMIC), pp 1–6. Doi:https://doi.org/10.1109/INMIC50486.2020.9318106

2. Albulayhi K, Smadi AA, Sheldon FT, Abercrombie RK (2021) IoT intrusion detection taxonomy, reference architecture, and analyses. Sensors 21(19):19. Doi:https://doi.org/10.3390/s21196432.

3. Bovenzi G, Aceto G, Ciuonzo D, Persico V, Pescapé A (2020) A hierarchical hybrid intrusion detection approach in IoT scenarios. In: GLOBECOM 2020—2020 IEEE global communications conference, pp 1–7. Doi:https://doi.org/10.1109/GLOBECOM42002.2020.9348167

4. Kan M (2016) Hackers create more IoT botnets with Mirai source code. In: Computerworld. Retrieved from https://www.computerworld.com/article/3132570/hackers-create-more-iot-botnets-with-mirai-source-code.html. Accessed on 26 Oct 2022

5. BrickerBot malware emerges, permanently bricks IoT devices—security news. Retrieved from https://www.trendmicro.com/vinfo/us/security/news/internet-of-things/brickerbot-malware-permanently-bricks-iot-devices. Accessed on 28 Oct 2022

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