Towards Interpretable Machine-Learning-Based DDoS Detection-Reference-Cited by-同舟云学术

Towards Interpretable Machine-Learning-Based DDoS Detection

Published:2023-12-18 Issue:1 Volume:5 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Zhou Qianru^ORCID,Li Rongzhen,Xu Lei,Nallanathan Arumugam,Yang Jian,Fu Anmin

Funder

Innovative Research Group Project of the National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s42979-023-02383-y.pdf

Reference36 articles.

1. Adeniji OD, Adekeye DB, Ajagbe SA, Adesina AO, Oguns YJ, Oladipupo MA. Development of ddos attack detection approach in software defined network using support vector machine classifier. In: Pervasive computing and social networking: Proceedings of ICPCSN 2022, Springer; 2022, p. 319–331.

2. Ajagbe SA, Awotunde JB, Oladipupo MA, Oye OE. Prediction and forecasting of coronavirus cases using artificial intelligence algorithm. In: machine learning for critical internet of medical things: applications and use cases, Springer; 2022, p. 31–54.

3. Ribeiro MT, Singh S, Guestrin C. “why should i trust you?” explaining the predictions of any classifier. In: 22nd ACM SIGKDD, 2016; p. 1135–1144.

4. Ribeiro MT, Singh S, Guestrin C. Anchors: high-precision model-agnostic explanations. In: AAAI. 2018;32.

5. Marino DL, Wickramasinghe CS, Manic M. An adversarial approach for explainable ai in intrusion detection systems. In: IEEE IECON, IEEE, 2018; p. 3237–3243.

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