Effective DDoS Mitigation via ML-Driven In-Network Traffic Shaping

Author:

Zhao Ziming1ORCID,Liu Zhuotao2ORCID,Chen Huan1ORCID,Zhang Fan1ORCID,Song Zhuoxue1ORCID,Li Zhaoxuan3ORCID

Affiliation:

1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

2. Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China

3. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

State Key Laboratory of Mathematical Engineering and Advanced Computing

Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness

Natural Science Foundation of Jiangsu Province

Fok Ying-Tung Education Foundation for Young Teachers in the Higher Education Institutions of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Poster: Combine Topology and Traffic to Calibrate P2P Botnet Identification in Large-Scale Network;Proceedings of the ACM SIGCOMM 2024 Conference: Posters and Demos;2024-08-04

2. RIDS: Towards Advanced IDS via RNN Model and Programmable Switches Co-Designed Approaches;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20

3. An Automated Alert Cross-Verification System with Graph Neural Networks for IDS Events;2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD);2024-05-08

4. DDoSMiner: An Automated Framework for DDoS Attack Characterization and Vulnerability Mining;Lecture Notes in Computer Science;2024

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