A Risk Assessment Optimization Model Based on TOPSIS Algorithm

Author:

Liu Yunkai1ORCID

Affiliation:

1. College of Science, Wuhan University of Technology, China

Publisher

ACM

Reference14 articles.

1. Network traffic anomaly detection based on time series analysis [J]. Journal of Jilin University;Wei Yan;Science Edition,2017

2. WANG Guo-lin, DU Li-ming, WANG Wei-min, WANG Feng-ying. Anomaly detection of industrial data based on improved AnomalyDetect algorithm [C] / / . Proceedings of the 19th Shenyang Scientific Annual Conference. 2022: 433-439 .DOI : 10.26914 / c.cnkihy. 2022 .016465. WANG Guo-lin, DU Li-ming, WANG Wei-min, WANG Feng-ying. Anomaly detection of industrial data based on improved AnomalyDetect algorithm [C] / /. Proceedings of the 19th Shenyang Scientific Annual Conference. 2022: 433-439.DOI : 10.26914 / c.cnkihy.2022.016465.

3. Analysis and modeling of industrial control network traffic characteristics [J];Yingxu Lai;Journal of Beijing University of Technology,2015

4. Li Y, Lu Y.Lstm-ba : Ddos detection approach combining lstm and bayes [C].2019 Seventh International Conference on Advanced Cloud and Big Data (CBD) , 2019 : 180-185. Li Y, Lu Y.Lstm-ba : Ddos detection approach combining lstm and bayes [C].2019 Seventh International Conference on Advanced Cloud and Big Data (CBD), 2019 : 180-185.

5. Kim D Yang H Chung M. Squeezed convolutional variational autoencoder for unsupervised anomaly detection in edge device industrial internet of things [C].2018 International Conference on Information and Computer Technologies (ICICT) 2018. Kim D Yang H Chung M. Squeezed convolutional variational autoencoder for unsupervised anomaly detection in edge device industrial internet of things [C].2018 International Conference on Information and Computer Technologies (ICICT) 2018.

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