TY - JOUR
T1 - Automatically dismantling online dating fraud
AU - Suarez-Tangil, Guillermo
AU - Edwards, Matthew
AU - Peersman, Claudia
AU - Stringhini, Gianluca
AU - Rashid, Awais
AU - Whitty, Monica
N1 - Funding Information:
Manuscript received April 6, 2018; revised March 6, 2019 and May 30, 2019; accepted July 2, 2019. Date of publication July 22, 2019; date of current version November 26, 2019. This work was supported by the DAPM Detecting and Preventing Mass-Marketing Fraud (MMF) Award from the U.K. Engineering and Physical Sciences Research Council under Grant EP/N028112/1. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Anna Squicciarini. (Corresponding author: Guillermo Suarez-Tangil.) G. Suarez-Tangil is with King’s College London, London WC2R 2LS, U.K. (e-mail: [email protected]).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2019/7/22
Y1 - 2019/7/22
N2 - Online romance scams are a prevalent form of mass-marketing fraud in the West, and yet few studies have presented data-driven responses to this problem. In this type of scam, fraudsters craft fake profiles and manually interact with their victims. Because of the characteristics of this type of fraud and how dating sites operate, traditional detection methods (e.g., those used in spam filtering) are ineffective. In this paper, we investigate the archetype of online dating profiles used in this form of fraud, including their use of demographics, profile descriptions, and images, shedding light on both the strategies deployed by scammers to appeal to victims and the traits of victims themselves. Furthermore, in response to the severe financial and psychological harm caused by dating fraud, we develop a system to detect romance scammers on online dating platforms. This paper presents the first fully described system for automatically detecting this fraud. Our aim is to provide an early detection system to stop romance scammers as they create fraudulent profiles or before they engage with potential victims. Previous research has indicated that the victims of romance scams score highly on scales for idealized romantic beliefs. We combine a range of structured, unstructured, and deep-learned features that capture these beliefs in order to build a detection system. Our ensemble machine-learning approach is robust to the omission of profile details and performs at high accuracy (97%) in a hold-out validation set. The system enables development of automated tools for dating site providers and individual users.
AB - Online romance scams are a prevalent form of mass-marketing fraud in the West, and yet few studies have presented data-driven responses to this problem. In this type of scam, fraudsters craft fake profiles and manually interact with their victims. Because of the characteristics of this type of fraud and how dating sites operate, traditional detection methods (e.g., those used in spam filtering) are ineffective. In this paper, we investigate the archetype of online dating profiles used in this form of fraud, including their use of demographics, profile descriptions, and images, shedding light on both the strategies deployed by scammers to appeal to victims and the traits of victims themselves. Furthermore, in response to the severe financial and psychological harm caused by dating fraud, we develop a system to detect romance scammers on online dating platforms. This paper presents the first fully described system for automatically detecting this fraud. Our aim is to provide an early detection system to stop romance scammers as they create fraudulent profiles or before they engage with potential victims. Previous research has indicated that the victims of romance scams score highly on scales for idealized romantic beliefs. We combine a range of structured, unstructured, and deep-learned features that capture these beliefs in order to build a detection system. Our ensemble machine-learning approach is robust to the omission of profile details and performs at high accuracy (97%) in a hold-out validation set. The system enables development of automated tools for dating site providers and individual users.
KW - Computer security
UR - https://www.scopus.com/pages/publications/85069923210
U2 - 10.1109/TIFS.2019.2930479
DO - 10.1109/TIFS.2019.2930479
M3 - Article
AN - SCOPUS:85069923210
SN - 1556-6013
VL - 15
SP - 1128
EP - 1137
JO - IEEE Transactions on Information Forensics and Security
JF - IEEE Transactions on Information Forensics and Security
ER -
SDG 3 Good Health and Well-being