StrucTemp-GNN: An Intrusion Detection Framework in IoT Networks Using Dynamic Heterogeneous Graph Neural Networks
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-52426-4_2
Reference25 articles.
1. Abid, M.: IoT Security Challenges and Mitigations: An Introduction, October 2022
2. Alsoufi, M.A., et al.: Anomaly-based intrusion detection systems in IoT using deep learning: a systematic literature review. Appl. Sci. 11(18), 8383 (2021). ISSN 2076-3417. https://doi.org/10.3390/app11188383. https://www.mdpi.com/2076-3417/11/18/8383. Accessed 12 Apr 2022
3. Altaf, T., et al.: A new concatenated Multigraph Neural Network for IoT intrusion detection. Internet Things 22, 100818 (2023). ISSN 2542-6605. https://doi.org/10.1016/j.iot.2023.100818. https://linkinghub.elsevier.com/retrieve/pii/S2542660523001415. Accessed 20 Oct 2023
4. Bagui, S., Wang, X., Bagui, S.: Machine learning based intrusion detection for IoT Botnet. IJMLC 11(6), 399–406 (2021). ISSN 2010-3700. https://doi.org/10.18178/ijmlc.2021.11.6.1068. https://www.ijmlc.org/index.php?m=content &c=index &a=show &catid=117 &id=1256. Accessed 30 Dec 2022
5. Caville, E., et al.: Anomal-E: a self-supervised network intrusion detection system based on graph neural networks. Knowl.-Based Syst. 258, 110030 (2022). ISSN 0950-7051. https://doi.org/10.1016/j.knosys.2022.110030. https://linkinghub.elsevier.com/retrieve/pii/S0950705122011236. Accessed 24 Jan 2023
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