| Xing Jian (邢剑)* ** ***,Wang Shupeng* **,Ding Yu* **.[J].高技术通讯(英文),2020,26(4):367~371 |
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| Fraudulent phone call recognition method based on convolutional neural network |
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| DOI：10.3772/j.issn.1006-6748.2020.04.003 |
| 中文关键词: |
| 英文关键词: fraudulent phone call recognition, convolutional neural network (CNN), calling detail records (CDR), deep learning (DL), telephone fraud |
| 基金项目: |
| Author Name | Affiliation | | Xing Jian (邢剑)* ** *** | (*Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, P.R.China)
(**School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, P.R.China)
(***Xinjiang Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China, Urumqi 830000, P.R.China) | | Wang Shupeng* ** | (*Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, P.R.China)
(**School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, P.R.China) | | Ding Yu* ** | (*Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, P.R.China)
(**School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| With increasingly rampant telephone fraud activities, the social impact and economic losses caused to China have increased dramatically. Precise, convenient, and efficient fraudulent phone call recognition has become a challenge since telephone fraud became more varied and covert. To deal with this problem, many researchers have extracted some statistical features of telephone fraud behavior and proposed some machine learning algorithms on the field of fraudulent phone call recognition. In this paper, the calling detail records are utilized to construct a classifier for fraudulent phone call recognition. Meantime, a deep learning approach based on convolutional neural network (CNN) is proposed for better features learning and compared with the existing state-of-the-art machine learning algorithms. It learns phone number and call behavior features of telephone fraud, and improves the accuracy of classification. The evaluation results show that the proposed algorithm outperforms competitive algorithms. |
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