Mitigating data imbalance to improve the generalizability in IoT DDoS detection tasks
Author:
Funder
China Scholarship Council
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11227-023-05829-5.pdf
Reference54 articles.
1. Cheng H, Liu J, Xu T, Ren B, Mao J, Zhang W (2020) Machine learning based low-rate DDoS attack detection for SDN enabled IoT networks. Int J Sens Netw 34(1):56–69. https://doi.org/10.1504/IJSNET.2020.109720
2. Dao NN, Phan TV, Sa’ad U, Kim J, Bauschert T, Do DT, Cho S (2021) Securing heterogeneous IoT with intelligent DDoS attack behavior learning. IEEE Syst J 16(2):1974–1983
3. Leevy JL, Khoshgoftaar TM (2020) A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data. J Big Data 7(1):104. https://doi.org/10.1186/s40537-020-00382-x
4. Layeghy S, Portmann M (2022) On generalisability of machine learning-based network intrusion detection systems. arXiv preprint arXiv:2205.04112. https://doi.org/10.48550/arXiv.2205.04112
5. de Melo LH, de C Bertoli G, Pereira LA, Saotome O, Domingues MF, dos Santos AL (2022) Generalizing flow classification for distributed denial-of-service over different networks. In: GLOBECOM 2022-2022 IEEE Global Communications Conference (pp 879–884). IEEE. https://doi.org/10.1109/GLOBECOM48099.2022.10001530
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