Detecting In Situ Identity Fraud on Social Network Services: A Case Study With Facebook

Wu, Shih Hsiung; Chou, Man-Ju; Tseng, Chun-Hsiung; Lee, Yuh-Jye; Chen, Kuan-Ta

doi:10.1109/jsyst.2015.2504102

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…Consequently, an unintended byproduct of sharing such personal content has thrived. As a result of sharing photos, hometowns, e-mail addresses, phone numbers, education and employment statuses on SNS profiles, SNSs are highly targeted by hackers (Wu, Chou, Tseng, Lee, & Chen, 2014), which makes it relatively easy to commit identity theft (Javaro & Jasinski, 2014). This type of theft can cause financial damage and huge personal trauma, for instance, by utilizing personal information to obtain access to credit cards and utility services, make false claims for medical services under stolen social security numbers (Acquisti & Gross, 2009) and evade law enforcement by masquerading under others' credentials (Javaro & Jasinski, 2014).…”

Section: Introductionmentioning

confidence: 99%

Understanding the privacy behavior of adolescents on Facebook: The role of peers, popularity and trust

Hofstra

Corten

Tubergen

2016

Computers in Human Behavior

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Section: Introductionmentioning

confidence: 99%

Understanding the privacy behavior of adolescents on Facebook: The role of peers, popularity and trust

Hofstra

Corten

Tubergen

2016

Computers in Human Behavior

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“…The way in which OSN users interact with known users, close friends, and relatives is different from how they interact with unfamiliar users in the network. People of different roles may have different intention, the age group, the sex, whether the user is a parent or a student; all these factors can be termed as role‐driven behavior . This role‐driven behavior can be used to identify compromised OSN accounts.…”

Section: Survey Of Detection Approachesmentioning

confidence: 99%

Compromised account detection in online social networks: A survey

Velayudhan

Somasundaram

2019

Concurrency and Computation

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Summary Online Social Networks are an integral part of the modern society. Social networking sites are at the top rank in terms of usage out of the billions of websites hosted in the internet. The way in which criminal activities are interleaved to these networks are beyond our imagination. Securing these networks from cyber criminals and malicious attackers are a major concern nowadays. There are various security threats happening to the Online Social Network accounts, and account compromise is one among it. Account compromise can cause collateral damage to the user and to a mass community. Misusing the personal information of the users and exploiting their trust relationships within the network lead to serious problems. These compromised accounts are becoming a safe zone for criminals to launch attacks. The threats caused by these compromised accounts include spreading malware, spy advertisements, and espionage. In most cases, it becomes too late to detect that an account is compromised. Detection of these accounts is important in order to reduce the impact of damage. The detection of compromised Online Social Network account is the process of confirming whether the account used by an individual or by an organization has lost its control or not. This survey reviews the various methods that are found in the literature toward detecting compromised Online Social Network Accounts.

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“…Although their detection algorithm appears feasible, they are limited in evaluation due to the fact that they cannot collect information about actual attacks nor can they simulate an attack on Facebook themselves. In the work of [210] they sought to circumvent these issues by having actual Facebook users participate in an experiment, browsing their own Facebook pages as well as browsing a stranger's. Given that intruders are likely to show different click behavior than legitimate users, they propose a detection scheme based on Smooth Support Vector Machines (SSVM).…”

Section: Cybersecurity Eventsmentioning

confidence: 99%

“…Research on detecting cybersecurity events mirrors work on SM seen elsewhere. The most impressive results come when SM behaviors are used to detect irregular activity representative of an account hijacking as in [210]. Behavioral markers of the type they utilize appear to be quite powerful in prediction, but wide-scale analysis of such systems cannot be done by outside researchers.…”

Section: Cybersecurity Eventsmentioning

confidence: 99%

Using Social Media to Predict the Future: A Systematic Literature Review

Phillips¹,

Dowling²,

Shaffer³

et al. 2017

Preprint

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Social media (SM) data provides a vast record of humanity's everyday thoughts, feelings, and actions at a resolution previously unimaginable. Because user behavior on SM is a reflection of events in the real world, researchers have realized they can use SM in order to forecast, making predictions about the future. The advantage of SM data is its relative ease of acquisition, large quantity, and ability to capture socially relevant information, which may be difficult to gather from other data sources. Promising results exist across a wide variety of domains, but one will find little consensus regarding best practices in either methodology or evaluation. In this systematic review, we examine relevant literature over the past decade, tabulate mixed results across a number of scientific disciplines, and identify common pitfalls and best practices. We find that SM forecasting is limited by data biases, noisy data, lack of generalizable results, a lack of domain-specific theory, and underlying complexity in many prediction tasks. But despite these shortcomings, recurring findings and promising results continue to galvanize researchers and demand continued investigation. Based on the existing literature, we identify research practices which lead to success, citing specific examples in each case and making recommendations for best practices. These recommendations will help researchers take advantage of the exciting possibilities offered by SM platforms.

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Detecting In Situ Identity Fraud on Social Network Services: A Case Study With Facebook

Cited by 12 publications

References 28 publications

Understanding the privacy behavior of adolescents on Facebook: The role of peers, popularity and trust

Understanding the privacy behavior of adolescents on Facebook: The role of peers, popularity and trust

Compromised account detection in online social networks: A survey

Using Social Media to Predict the Future: A Systematic Literature Review

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