Mobile network risk user recognition based on ensemble learning

Author:

Wu Kaili1,Xu Xueqi1,Wang Zhouxiang1

Affiliation:

1. Zhejiang Gongshang University, Hangzhou, China

Publisher

ACM Press

Reference15 articles.

1. Wang Peng, Song Yanhong, Li Songjiang, Yang Huaming, Qiu Ningjia. 2017. Detection method for abnormal user of social network based on behavior characteristics. Journal of Computer Applications. (December.2017), 219--224.

2. Xu Bing, Guo Yuanbo, Ye Ziwei, Hu Yongjin. 2018. Abnormal User detection in enterprise network based on graph analysis and support vector machine. Journal of Computer Applications. (February. 2018), 357--362.

3. Chen Hongsong, Wang Gang, Song Jianlin.2018. Research on Anomaly Behavior Classification Algorithm of Internal Network User Based on Cloud Computing Intrusion Detection Data Set. Netinfo Security, (March.2018). 1--7.

4. Li Haibing, Li Qi, Tang ruming, et al. 2018. User Behavior Anomaly Detection for Database Based on Unsupervised Learning. Journal of Chinese Computer Systems. (November. 2018). 2464--2472.

5. Yang Xiaohui, Guo Binghui, Mi Zhilong, Zheng Zhiming. 2019. Identification algorithm of high breaching risk member for Internet financial platform. http://www.arocmag.com/article/02-2019-03-025.html

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