[1] 赵军, 张建肖. 网络黑灰产治理须多管齐下[J]. 中国信息安全, 2017(12):73-74. Zhao J, Zhang J X. The management of network black ash production should be multi pronged[J]. China Information Security, 2017(12):73-74. (in Chinese) [2] 赵宇飞, 邓中豪. 谨防信用卡套现"里应外合"[J]. 今日南国, 2014(4):11. Zhao Y F, Deng Z H. Guard against "internal and external cooperation" of credit card fraud[J]. The South of China Today, 2014(4):11. (in Chinese) [3] 尚丹. 2017年国家网络安全宣传周新亮点[J]. 信息系统工程, 2017(10):8-10. Shang D. New highlights of national cyber security week in 2017[J]. China CIO News, 2017(10):8-10. (in Chinese) [4] 戴先任. 别让网络黑灰产业恶化网络生态[J]. 青年记者, 2017(22):63. Dai X R. Don't let the network black gray industry worsen the network ecology[J]. Youth Journalist, 2017(22):63. (in Chinese) [5] 叶菁. 大数据与黑灰产规模齐飞, 保护隐私需三管齐下[J]. 中国报业, 2018, 456(23):107. Ye Q. Big data and black ash production scale fly together, privacy protection needs three pronged approach[J]. China Newspaper Industry, 2018, 456(23):107. (in Chinese) [6] 高增安. 基于交易的可疑洗钱行为模式与反洗钱对策研究[D]. 成都:西南交通大学, 2007. [7] 徐程佳. 基于指纹识别的银行卡交易欺诈侦测系统的优化方案的研究[D]. 上海:华东师范大学, 2013. [8] Sánchez D, Vila M A, Cerda L, et al. Association rules applied to credit card fraud detection[J]. Expert Systems with Applications, 2009, 36(2):3630-3640. [9] Weng H, Li Z, Ji S, et al. Online e-commerce fraud:a large-scale detection and analysis[C]//2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018:1435-1440. [10] Bekirev A S, Klimov V V, Kuzin M V, et al. Payment card fraud detection using neural network committee and clustering[J]. Optical Memory and Neural Networks, 2015, 24(3):193-200. [11] Šubelj L, Furlan Š, Bajec M. An expert system for detecting automobile insurance fraud using social network analysis[J]. Expert Systems with Applications, 2011, 38(1):1039-1052. [12] Hilas C S, Mastorocostas P A, Rekanos I T. Clustering of telecommunications user profiles for fraud detection and security enhancement in large corporate networks:a case study[J]. Applied Mathematics & Information Sciences, 2015, 9(4):1709. [13] Bahnsen A C, Stojanovic A, Aouada D, et al. Improving credit card fraud detection with calibrated probabilities[J]. SIAM International Conference on Data Mining, 2014:677-685. [14] 孙万龙. 基于GBDT的社区问题标签推荐技术研究[D]. 哈尔滨:哈尔滨工业大学, 2015. [15] 蔡文学, 罗永豪, 张冠湘, 等. 基于GBDT与Logistic回归融合的个人信贷风险评估模型及实证分析[J]. 管理现代化, 2017(2):3-4. Cai W X, Luo Y H, Zhang G X. Personal credit risk assessment model and empirical analysis based on gbdt and logistic regression[J]. Modernization of Management, 2017(2):3-4. (in Chinese) |