Telecommunication fraud has run rampant recently worldwide.However,previous studies depend highly on expert knowledge-based feature engineering to extract behavior information,which cannot adapt to the fastchanging mo...Telecommunication fraud has run rampant recently worldwide.However,previous studies depend highly on expert knowledge-based feature engineering to extract behavior information,which cannot adapt to the fastchanging modes of fraudulent subscribers.Therefore,we propose a new taxonomy that needs no hand-designed features but directly takes raw Call DetailRecords(CDR)data as input for the classifier.Concretely,we proposed a fraud detectionmethod using a convolutional neural network(CNN)by taking CDR data as images and applying computer vision techniques like image augmentation.Comprehensive experiments on the real-world dataset from the 2020 Digital Sichuan Innovation Competition show that our proposed method outperforms the classic methods in many metrics with excellent stability in both the changes of quantity and the balance of samples.Compared with the state-of-the-art method,the proposed method has achieved about 89.98%F1-score and 92.93%AUC,improving 2.97%and 0.48%,respectively.With the augmentation technique,the model’s performance can be further enhanced by a 91.09%F1-score and a 94.49%AUC respectively.Beyond telecommunication fraud detection,our method can also be extended to other text datasets to automatically discover new features in the view of computer vision and its powerful methods.展开更多
Against the backdrop of efforts to significantly enhance the modern combat capabilities of public security,this paper focuses on the interrogation challenges in cross-border telecommunication network fraud cases and e...Against the backdrop of efforts to significantly enhance the modern combat capabilities of public security,this paper focuses on the interrogation challenges in cross-border telecommunication network fraud cases and explores the application of Artificial Intelligence(AI)technology to improve interrogation efficiency and quality.It proposes the construction of an intelligent interrogation system by identifying the criminal characteristics of cross-border telecommunication fraud and analyzing the current technical and practical difficulties faced in interrogations.Focusing on AI-assisted interrogation in telecommunication fraud investigations,the study develops algorithms and models to build an intelligent interrogation system for cross-border fraud cases.The results indicate that AI-based interrogation systems can enhance the efficiency of information extraction,optimize investigative strategies,and provide technological support for the rapid resolution of cross-border telecommunication fraud cases.展开更多
基金This research was funded by the Double Top-Class Innovation research project in Cyberspace Security Enforcement Technology of People’s Public Security University of China(No.2023SYL07).
文摘Telecommunication fraud has run rampant recently worldwide.However,previous studies depend highly on expert knowledge-based feature engineering to extract behavior information,which cannot adapt to the fastchanging modes of fraudulent subscribers.Therefore,we propose a new taxonomy that needs no hand-designed features but directly takes raw Call DetailRecords(CDR)data as input for the classifier.Concretely,we proposed a fraud detectionmethod using a convolutional neural network(CNN)by taking CDR data as images and applying computer vision techniques like image augmentation.Comprehensive experiments on the real-world dataset from the 2020 Digital Sichuan Innovation Competition show that our proposed method outperforms the classic methods in many metrics with excellent stability in both the changes of quantity and the balance of samples.Compared with the state-of-the-art method,the proposed method has achieved about 89.98%F1-score and 92.93%AUC,improving 2.97%and 0.48%,respectively.With the augmentation technique,the model’s performance can be further enhanced by a 91.09%F1-score and a 94.49%AUC respectively.Beyond telecommunication fraud detection,our method can also be extended to other text datasets to automatically discover new features in the view of computer vision and its powerful methods.
基金Shanxi Province Preferential Funding Program for Scientific and Technological Activities of Returned Overseas Students(20240038)Shanxi Police College 2024 Undergraduate Innovation Training Program(2024XY39)。
文摘Against the backdrop of efforts to significantly enhance the modern combat capabilities of public security,this paper focuses on the interrogation challenges in cross-border telecommunication network fraud cases and explores the application of Artificial Intelligence(AI)technology to improve interrogation efficiency and quality.It proposes the construction of an intelligent interrogation system by identifying the criminal characteristics of cross-border telecommunication fraud and analyzing the current technical and practical difficulties faced in interrogations.Focusing on AI-assisted interrogation in telecommunication fraud investigations,the study develops algorithms and models to build an intelligent interrogation system for cross-border fraud cases.The results indicate that AI-based interrogation systems can enhance the efficiency of information extraction,optimize investigative strategies,and provide technological support for the rapid resolution of cross-border telecommunication fraud cases.
