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Evaluation of the Attack Effect Based on Improved Grey Clustering Model

Evaluation of the Attack Effect Based on Improved Grey Clustering Model

Chen Yue, Lu Tianliang, Cai Manchun, Li Jingying
Copyright: © 2021 |Pages: 9
ISBN13: 9781799853480|ISBN10: 1799853489|EISBN13: 9781799853497
DOI: 10.4018/978-1-7998-5348-0.ch016
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MLA

Yue, Chen, et al. "Evaluation of the Attack Effect Based on Improved Grey Clustering Model." Research Anthology on Combating Denial-of-Service Attacks, edited by Information Resources Management Association, IGI Global, 2021, pp. 302-310. https://doi.org/10.4018/978-1-7998-5348-0.ch016

APA

Yue, C., Tianliang, L., Manchun, C., & Jingying, L. (2021). Evaluation of the Attack Effect Based on Improved Grey Clustering Model. In I. Management Association (Ed.), Research Anthology on Combating Denial-of-Service Attacks (pp. 302-310). IGI Global. https://doi.org/10.4018/978-1-7998-5348-0.ch016

Chicago

Yue, Chen, et al. "Evaluation of the Attack Effect Based on Improved Grey Clustering Model." In Research Anthology on Combating Denial-of-Service Attacks, edited by Information Resources Management Association, 302-310. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-5348-0.ch016

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Abstract

There are a lot of uncertainties and incomplete information problems on network attack. It is of great value to access the effect of the attack in the current network attack and defense. This paper examines the characteristics of network attacks, there are problems with traditional clustering that index attribution is not clear and the cross of clustering interval. A two-stage grey synthetic clustering evaluation model based on center-point triangular whitenization weight function was proposed for the attack effect. The authors studied the feasibility of applying this model to the evaluation of network attack effect. Finally, an example is given, which showed the model could evaluate the effect of the denial-of-service attack precisely. It is also shown that the model is viable to evaluate the attack effect.

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