Browsing by Author "Huang, Che-Wei"

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FrauDetector(+): An Incremental Graph-Mining Approach for Efficient Fraudulent Phone Call Detection
(2018-10-01)
Ying, Josh Jia-Ching
;
Zhang, Ji
;
Huang, Che-Wei
;
Chen, Kuan-Ta
;
Tseng, Vincent S.
In recent years, telecommunication fraud has become more rampant internationally with the development of modern technology and global communication. Because of rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with big data issues in real-world implementations. Although our previous work, FrauDetector, addressed this problem and achieved some promising results, it can be further enhanced because it focuses only on fraud detection accuracy, whereas the efficiency and scalability are not top priorities. Other known approaches for fraudulent call number detection suffer from long training times or cannot accurately detect fraudulent phone calls in real time. However, the learning process of FrauDetector is too time-consuming to support real-world application. Although we have attempted to accelerate the the learning process of FrauDetector by parallelization, the parallelized learning process, namely PFrauDetector, still cannot afford the computing cost. In this article, we propose a highly efficient incremental graph-mining-based fraudulent phone call detection approach, namely FrauDetectoe , which can automatically label fraudulent phone numbers with a "fraud" tag a crucial prerequisite for distinguishing fraudulent phone call numbers from nonfraudulent ones. FratiDetectoe initially generates smaller, more manageable subnetworks from original graph and performs a parallelized weighted HITS algorithm for a significant speed increase in the graph learning module. It adopts a novel aggregation approach to generate a trust (or experience) value for each phone number (or user) based on their respective local values. After the initial procedure, we can incrementally update the trust (or experience) value for each phone number (or user) while a new fraud phone number is identified. An efficient fraud-centric hash structure is constructed to support fast real-time detection of fraudulent phone numbers in the detection module. We conduct a comprehensive experimental study based on real datasets collected through an antifraud mobile application called Whoscall. The results demonstrate a significantly improved efficiency of our approach compared with FrauDetector as well as superior performance against other major classifier-based methods.
FrauDetector: A Graph-Mining-based Framework for Fraudulent Phone Call Detection
(2015-01-01)
Tseng, Vincent S.
;
Ying, Josh Jia-Ching
;
Huang, Che-Wei
;
Kao, Yimin
;
Chen, Kuan-Ta
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.
PFrauDetector: A Parallelized Graph Mining Approach for Efficient Fraudulent Phone Call Detection
(2016)
Ying, Josh Jia-Ching
;
Zhang, Ji
;
Huang, Che-Wei
;
Chen, Kuan-Ta
;
Tseng, Vincent S.
In recent years, fraud is becoming more rampant internationally with the development of modern technology and global communication. Due to the rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with Big Data issues in real-world implementations. While our previous work, FrauDetector, has addressed this problem and achieved some promising results, it can be further enhanced as it focuses on the fraud detection accuracy while the efficiency and scalability are not on the top priority. Meanwhile, other known approaches suffer from long training time and/or cannot accurately detect fraudulent phone calls in real time. In this paper, we propose a highly-efficient parallelized graph-mining-based fraudulent phone call detection framework, namely PFrauDetector, which is able to automatically label fraudulent phone numbers with a "fraud" tag, a crucial prerequisite for distinguishing fraudulent phone call numbers from the normal ones. PFrauDetector generates smaller, more manageable sub-networks from the original graph and performs a parallelized weighted HITS algorithm for significant speed acceleration in the graph learning module. It adopts a novel aggregation approach to generate the trust (or experience) value for each phone number (or user) based on their respective local values. We conduct a comprehensive experimental study based on a real dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate a significantly improved efficiency of our approach compared to FrauDetector and superior performance against other major classifier-based methods.