標題: FrauDetector: A Graph-Mining-based Framework for Fraudulent Phone Call Detection
作者: Tseng, Vincent S.
Ying, Josh Jia-Ching
Huang, Che-Wei
Kao, Yimin
Chen, Kuan-Ta
資訊工程學系
Department of Computer Science
關鍵字: Telecommunication Fraud;Trust Value Mining;Fraudulent Phone Call Detection;Weighted HITS Algorithm
公開日期: 1-一月-2015
摘要: In recent years, fraud is increasing rapidly with the development of modern technology and global communication. Although many literatures have addressed the fraud detection problem, these existing works focus only on formulating the fraud detection problem as a binary classification problem. Due to limitation of information provided by telecommunication records, such classifier-based approaches for fraudulent phone call detection normally do not work well. In this paper, we develop a graph-mining-based fraudulent phone call detection framework for a mobile application to automatically annotate fraudulent phone numbers with a "fraud" tag, which is a crucial prerequisite for distinguishing fraudulent phone calls from normal phone calls. Our detection approach performs a weighted HITS algorithm to learn the trust value of a remote phone number. Based on telecommunication records, we build two kinds of directed bipartite graph: i) CPU and ii) UPG to represent telecommunication behavior of users. To weight the edges of CPU and UPG, we extract features for each pair of user and remote phone number in two different yet complementary aspects: I) duration relatedness (DR) between user and phone number; and 2) frequency relatedness (FR) between user and phone number. Upon weighted CPG and UPG, we determine a trust value for each remote phone number. Finally, we conduct a comprehensive experimental study based on a dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate the effectiveness of our weighted HITS-based approach and show the strength of taking both DR and FR into account in feature extraction.
URI: http://dx.doi.org/10.1145/2783258.2788623
http://hdl.handle.net/11536/152979
ISBN: 978-1-4503-3664-2
DOI: 10.1145/2783258.2788623
期刊: KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING
起始頁: 2157
結束頁: 2166
顯示於類別:會議論文