一種基于圖神經網絡的電信詐騙識別方法
2021年電子技術應用第6期
張杰俊1,唐穎淳1,季述鄖2,李靜林2
1.中國電信股份有限公司上海分公司,上海200041; 2.北京郵電大學 網絡與交換技術國家重點實驗室,北京100876
摘要: 通信技術的普及給人們帶來便捷的同時,電信欺詐行為也急劇增加。由于詐騙行為特征、號碼類型等與正常業務具有極高相似性,傳統基于統計的電信欺詐檢測方法難于篩選。提出將用戶通信關系轉換為一組拓撲特征,建立通信社交有向圖,將具有統計特征的頂點表示用戶,具有關系特征的邊表示他們之間的活動。在通信社交圖基礎上,通過圖卷積模塊捕獲用戶的通信行為規律和通信社交關系特征,通過池化讀出機制聚合通信社交網絡的潛在特征,以識別電信欺詐行為。真實通信歷史數據驗證表明了該方法的有效性。
中圖分類號: TP18;F626
文獻標識碼: A
DOI:10.16157/j.issn.0258-7998.200976
中文引用格式: 張杰俊,唐穎淳,季述鄖,等. 一種基于圖神經網絡的電信詐騙識別方法[J].電子技術應用,2021,47(6):25-29,34.
英文引用格式: Zhang Jiejun,Tang Yingchun,Ji Shuyun,et al. A telecom fraud identification method based on graph neural net-
work[J]. Application of Electronic Technique,2021,47(6):25-29,34.
文獻標識碼: A
DOI:10.16157/j.issn.0258-7998.200976
中文引用格式: 張杰俊,唐穎淳,季述鄖,等. 一種基于圖神經網絡的電信詐騙識別方法[J].電子技術應用,2021,47(6):25-29,34.
英文引用格式: Zhang Jiejun,Tang Yingchun,Ji Shuyun,et al. A telecom fraud identification method based on graph neural net-
work[J]. Application of Electronic Technique,2021,47(6):25-29,34.
A telecom fraud identification method based on graph neural network
Zhang Jiejun1,Tang Yingchun1,Ji Shuyun2,Li Jinglin2
1.China Telecom Corporation Limited Shanghai Branch,Shanghai 200041,China; 2.State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications, Beijing 100876,China
Abstract: While communication technology brings convenience to people, telecom fraud also increases sharply. Traditional detection methods are mainly based on data mining and statistical learning of history data. However, due to the high similarity between fraud behavior and normal business, traditional statistical methods are difficult to screen. This paper proposes to transform user communication relationship into a set of topological features and establish communication social directed graph, where vertices with statistical characteristics represent users and edges with relational characteristics represent activities between them. On the basis of the communication social graph, the potential characteristics of the communication social network are learned through the graph neural network, and the information characteristics of multiple nodes are aggregated through pooling readout mechanism, in order to identify the telecom fraud users. The validation of real communication history data shows the effectiveness of this method.
Key words : fraud detection;communication social network;graph neural networks;behavior classification
0 引言
隨著信息社會的發展,電信欺詐高發,但由于通信關系的復雜性和不確定性,電信欺詐檢測成為了一個十分困難的問題。
傳統電信欺詐檢測技術主要基于用戶屬性和通話記錄來獲得用戶行為樣本,再通過SVM、LGB等機器學習方法學習行為特征[1-2]。這些方法主要使用短時間的行為統計進行分類,往往會出現時間尺度特征不足的問題。同時,由于用戶通話行為的復雜性,以固定窗口的統計特征作為詐騙電話的統計依據[3-4],容易受到長期行為變化影響,分類效果差。
本文詳細內容請下載:http://www.rjjo.cn/resource/share/2000003568。
作者信息:
張杰俊1,唐穎淳1,季述鄖2,李靜林2
(1.中國電信股份有限公司上海分公司,上海200041;
2.北京郵電大學 網絡與交換技術國家重點實驗室,北京100876)
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