{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T17:50:38Z","timestamp":1760550638732,"version":"3.41.0"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2018,8,28]],"date-time":"2018-08-28T00:00:00Z","timestamp":1535414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan, R.O.C.","doi-asserted-by":"crossref","award":["104-2221-E-009-128-MY3 and 106-2221-E-035-094-"],"award-info":[{"award-number":["104-2221-E-009-128-MY3 and 106-2221-E-035-094-"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2018,12,31]]},"abstract":"<jats:p>\n            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,\n            <jats:italic>FrauDetector<\/jats:italic>\n            , 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\n            <jats:italic>FrauDetector<\/jats:italic>\n            is too time-consuming to support real-world application. Although we have attempted to accelerate the the learning process of\n            <jats:italic>FrauDetector<\/jats:italic>\n            by parallelization, the parallelized learning process, namely\n            <jats:italic>PFrauDetector<\/jats:italic>\n            , still cannot afford the computing cost. In this article, we propose a highly efficient incremental graph-mining-based fraudulent phone call detection approach, namely\n            <jats:italic>FrauDetector<\/jats:italic>\n            <jats:sup>+<\/jats:sup>\n            , which can automatically label fraudulent phone numbers with a \u201cfraud\u201d tag a crucial prerequisite for distinguishing fraudulent phone call numbers from nonfraudulent ones.\n            <jats:italic>FrauDetector<\/jats:italic>\n            <jats:sup>+<\/jats:sup>\n            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\n            <jats:italic>Whoscall<\/jats:italic>\n            . The results demonstrate a significantly improved efficiency of our approach compared with\n            <jats:italic>FrauDetector<\/jats:italic>\n            as well as superior performance against other major classifier-based methods.\n          <\/jats:p>","DOI":"10.1145\/3234943","type":"journal-article","created":{"date-parts":[[2018,8,30]],"date-time":"2018-08-30T13:45:11Z","timestamp":1535636711000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["<i>FrauDetector<\/i>\n            <sup>+<\/sup>"],"prefix":"10.1145","volume":"12","author":[{"given":"Josh Jia-Ching","family":"Ying","sequence":"first","affiliation":[{"name":"National Yunlin University of Science and Technology, Douliou, Taiwan"}]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, The University of Southern Queensland, Toowoomba, QLD, Australia"}]},{"given":"Che-Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"National Cheng Kung University, Tainan, Taiwan"}]},{"given":"Kuan-Ta","family":"Chen","sequence":"additional","affiliation":[{"name":"Academia Sinica, Taipei, Taiwan"}]},{"given":"Vincent S.","family":"Tseng","sequence":"additional","affiliation":[{"name":"National Chiao Tung University, Hsinchu, Taiwan"}]}],"member":"320","published-online":{"date-parts":[[2018,8,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the International Workshop on Advanced Compiler Technology for High Performance and Embedded Processors.","author":"An Ping","year":"2001","unstructured":"Ping An , Alin Jula , Silvius Rus , Steven Saunders , Tim Smith , Gabriel Tanase , Nathan Thomas , Nancy Amato , and Lawrence Rauchwerger . 2001 a. 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