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An approach for detecting LDoS attack based on cloud model

Frontiers of Computer Science volume 16, Article number: 166821 (2022) Cite this article

Abstract

Cybersecurity has always been the focus of Internet research. An LDoS attack is an intelligent type of DoS attack, which reduces the quality of network service by periodically sending high-speed but short-pulse attack traffic. Because of its concealment and low average rate, the traditional DoS attack detection methods are challenging to be effective. The existing LDoS attack detection methods generally have the problems of high FPR and FNR. A cloud model-based LDoS attack detection method is proposed, and a classifier based on SVM is used to train and classify the feature parameters. The detection method is verified and tested in the NS2 simulation platform and Test-bed network environment. Compared with the existing research results, the proposed method requires fewer samples, and it has lower FPR and FNR.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61772189,61772191), and the Hunan Provincial Natural Science Foundation of China (2019JJ40037).

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Authors and Affiliations

  1. The College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Wei Shi, Dan Tang, Sijia Zhan, Zheng Qin & Xiyin Wang

Authors
  1. Wei Shi
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  2. Dan Tang
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  3. Sijia Zhan
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  4. Zheng Qin
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  5. Xiyin Wang
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Corresponding author

Correspondence to Wei Shi.

Additional information

Wei Shi received bachelor’s degree in computer science and technology from Jiangxi Normal University, China in 2018. She is now a postgraduate student in College of Computer Science and Electronic Engineering, Hunan University, China. The current research direction is network attack detection.

Dan Tang is an associate professor of College of Computer Science and Electronic Engineering, Hunan University, China. He received the PhD degree from Huazhong University of Science and Technology, China in 2014. His research interests include the areas of computer network security, computer information security, and architecture of future Internet.

Sijia Zhan received the BS degree in software engineering from South-central University for nationalities, China in 2017. She is a currently master degree candidate in College of Computer Science and Electronic Engineering, Hunan University, China. Her current research interest is network security.

Zheng Qin received the PhD degree in computer software and theory from Chongqin University, China in 2001. He is a professor of computer science and technology in Hunan University, China. He is a member of China Computer Federation (CCF) and ACM. His main interests are computer network and information security, cloud computing, big data processing and software engineering. He has accumulated rich experience in products development and application services, such as in the area of financial, medical, military and education sectors.

Xiyin Wang entered Hunan Normal University, China in September 2015. She is currently a senior majoring in Electronic Commerce in Hunan normal university, China. Her research direction is cyber-space security.

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Cite this article

Shi, W., Tang, D., Zhan, S. et al. An approach for detecting LDoS attack based on cloud model. Front. Comput. Sci. 16, 166821 (2022). https://doi.org/10.1007/s11704-022-0486-1

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  • DOI: https://doi.org/10.1007/s11704-022-0486-1

Keywords

  • cybersecurity
  • LDoS attack
  • cloud model
  • SVM