Enhancing SIoT Security Through Advanced Machine Learning Techniques for Intrusion Detection
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2053-8_8
Reference21 articles.
1. Zhao Y, Dong X, Yin Y (2019) Effective and efficient dense subgraph query in large-scale social internet of things. IEEE Trans Industr Inf 16(4):2726–2736
2. Mbona I, Eloff JH (2022) Detecting zero-day intrusion attacks using semi-supervised machine learning approaches. IEEE Access 10:69822–69838
3. Rehman E, Haseeb-ud-Din M, Malik AJ, Khan TK, Abbasi AA, Kadry S, Rho S (2022) Intrusion detection based on machine learning in the internet of things, attacks and countermeasures. J Supercomput, pp 1–35
4. Pu G, Wang L, Shen J, Dong F (2020) A hybrid unsupervised clustering-based anomaly detection method. Tsinghua Sci Technol 26(2):146–153
5. Azad MA, Bag S, Hao F, Shalaginov A (2020) Decentralized self-enforcing trust management system for social Internet of Things. IEEE Internet Things J 7(4):2690–2703
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