Fraud Analysis and Detection for Real-Time Messaging Communications on Social Networks

Liang-Chun CHEN; Chien-Lung HSU; Nai-Wei LO; Kuo-Hui YEH; Ping-Hsien LIN

doi:10.1587/transinf.2016INI0003

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Fraud Analysis and Detection for Real-Time Messaging Communications on Social Networks

Liang-Chun CHEN, Chien-Lung HSU, Nai-Wei LO, Kuo-Hui YEH, Ping-Hsien LIN

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Summary :

With the successful development and rapid advancement of social networking technology, people tend to exchange and share information via online social networks, such as Facebook and LINE.Massive amounts of information are aggregated promptly and circulated quickly among people. However, with the enormous volume of human-interactions, various types of swindles via online social networks have been launched in recent years. Effectively detecting fraudulent activities on social networks has taken on increased importance, and is a topic of ongoing interest. In this paper, we develop a fraud analysis and detection system based on real-time messaging communications, which constitute one of the most common human-interacted services of online social networks. An integrated platform consisting of various text-mining techniques, such as natural language processing, matrix processing and content analysis via a latent semantic model, is proposed. In the system implementation, we first collect a series of fraud events, all of which happened in Taiwan, to construct analysis modules for detecting such fraud events. An Android-based application is then built for alert notification when dubious logs and fraud events happen.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.10 pp.2267-2274

Publication Date: 2017/10/01

Publicized: 2017/07/21

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016INI0003

Type of Manuscript: Special Section INVITED PAPER (Special Section on Security, Privacy and Anonymity in Computation, Communication and Storage Systems)

Category

Authors

Liang-Chun CHEN
  Fo Guang University
Chien-Lung HSU
  Chang Gung University
Nai-Wei LO
  National Taiwan University of Science and Technology
Kuo-Hui YEH
  National Dong Hwa University
Ping-Hsien LIN
  National Taiwan University of Science and Technology

Keyword

Facebook, fraud analysis, latent semantic analysis, natural language processing, social networks

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Liang-Chun CHEN, Chien-Lung HSU, Nai-Wei LO, Kuo-Hui YEH, Ping-Hsien LIN, "Fraud Analysis and Detection for Real-Time Messaging Communications on Social Networks" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 10, pp. 2267-2274, October 2017, doi: 10.1587/transinf.2016INI0003.
Abstract: With the successful development and rapid advancement of social networking technology, people tend to exchange and share information via online social networks, such as Facebook and LINE.Massive amounts of information are aggregated promptly and circulated quickly among people. However, with the enormous volume of human-interactions, various types of swindles via online social networks have been launched in recent years. Effectively detecting fraudulent activities on social networks has taken on increased importance, and is a topic of ongoing interest. In this paper, we develop a fraud analysis and detection system based on real-time messaging communications, which constitute one of the most common human-interacted services of online social networks. An integrated platform consisting of various text-mining techniques, such as natural language processing, matrix processing and content analysis via a latent semantic model, is proposed. In the system implementation, we first collect a series of fraud events, all of which happened in Taiwan, to construct analysis modules for detecting such fraud events. An Android-based application is then built for alert notification when dubious logs and fraud events happen.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016INI0003/_p

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@ARTICLE{e100-d_10_2267,
author={Liang-Chun CHEN, Chien-Lung HSU, Nai-Wei LO, Kuo-Hui YEH, Ping-Hsien LIN, },
journal={IEICE TRANSACTIONS on Information},
title={Fraud Analysis and Detection for Real-Time Messaging Communications on Social Networks},
year={2017},
volume={E100-D},
number={10},
pages={2267-2274},
abstract={With the successful development and rapid advancement of social networking technology, people tend to exchange and share information via online social networks, such as Facebook and LINE.Massive amounts of information are aggregated promptly and circulated quickly among people. However, with the enormous volume of human-interactions, various types of swindles via online social networks have been launched in recent years. Effectively detecting fraudulent activities on social networks has taken on increased importance, and is a topic of ongoing interest. In this paper, we develop a fraud analysis and detection system based on real-time messaging communications, which constitute one of the most common human-interacted services of online social networks. An integrated platform consisting of various text-mining techniques, such as natural language processing, matrix processing and content analysis via a latent semantic model, is proposed. In the system implementation, we first collect a series of fraud events, all of which happened in Taiwan, to construct analysis modules for detecting such fraud events. An Android-based application is then built for alert notification when dubious logs and fraud events happen.},
keywords={},
doi={10.1587/transinf.2016INI0003},
ISSN={1745-1361},
month={October},}

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TY - JOUR
TI - Fraud Analysis and Detection for Real-Time Messaging Communications on Social Networks
T2 - IEICE TRANSACTIONS on Information
SP - 2267
EP - 2274
AU - Liang-Chun CHEN
AU - Chien-Lung HSU
AU - Nai-Wei LO
AU - Kuo-Hui YEH
AU - Ping-Hsien LIN
PY - 2017
DO - 10.1587/transinf.2016INI0003
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2017
AB - With the successful development and rapid advancement of social networking technology, people tend to exchange and share information via online social networks, such as Facebook and LINE.Massive amounts of information are aggregated promptly and circulated quickly among people. However, with the enormous volume of human-interactions, various types of swindles via online social networks have been launched in recent years. Effectively detecting fraudulent activities on social networks has taken on increased importance, and is a topic of ongoing interest. In this paper, we develop a fraud analysis and detection system based on real-time messaging communications, which constitute one of the most common human-interacted services of online social networks. An integrated platform consisting of various text-mining techniques, such as natural language processing, matrix processing and content analysis via a latent semantic model, is proposed. In the system implementation, we first collect a series of fraud events, all of which happened in Taiwan, to construct analysis modules for detecting such fraud events. An Android-based application is then built for alert notification when dubious logs and fraud events happen.
ER -