Privacy and security in online social networks: A survey

Kayes, Imrul; Iamnitchi, Adriana

doi:10.1016/j.osnem.2017.09.001

Cited by 99 publications

(75 citation statements)

References 123 publications

(144 reference statements)

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“…Protecting users' personal information is an essential element in promoting sustainable use of social networks (Kayes and Iamnitchi 2017). SN providers should provide better privacy rules and policies and develop more effective security and privacy settings.…”

Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

Predicting individuals’ vulnerability to social engineering in social networks

Albladi

Weir

2020

Cybersecur

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The popularity of social networking sites has attracted billions of users to engage and share their information on these networks. The vast amount of circulating data and information expose these networks to several security risks. Social engineering is one of the most common types of threat that may face social network users. Training and increasing users' awareness of such threats is essential for maintaining continuous and safe use of social networking services. Identifying the most vulnerable users in order to target them for these training programs is desirable for increasing the effectiveness of such programs. Few studies have investigated the effect of individuals' characteristics on predicting their vulnerability to social engineering in the context of social networks. To address this gap, the present study developed a novel model to predict user vulnerability based on several perspectives of user characteristics. The proposed model includes interactions between different social network-oriented factors such as level of involvement in the network, motivation to use the network, and competence in dealing with threats on the network. The results of this research indicate that most of the considered user characteristics are factors that influence user vulnerability either directly or indirectly. Furthermore, the present study provides evidence that individuals' characteristics can identify vulnerable users so that these risks can be considered when designing training and awareness programs.

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Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

Predicting individuals’ vulnerability to social engineering in social networks

Albladi

Weir

2020

Cybersecur

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“…Millions of people use different forms of social networks that allow them to communicate with friends, relatives, and share personal information. However, popularity of social networks creates a great risk for their users (Fire, Goldschmidt, & Elovici, 2014;Kayes & Iamnitchi, 2017;Novak & Li, 2012;Rathore et al, 2017). Rapid increase in the amount of personal information shared by social network users turns them into a desirable target of the malicious users.…”

Section: Introductionmentioning

confidence: 99%

“…Rapid increase in the amount of personal information shared by social network users turns them into a desirable target of the malicious users. When uploading multimedia content such as user photos, videos and others, there may be various problems with the privacy and security of user data (Dreßing, et al, 2014;Fire, Goldschmidt, & Elovici, 2014;Gao, et al, 2011;Kayes & Iamnitchi, 2017). Uploaded multimedia content can carry information transmitted through the virus that begins distributing on the social network site and beyond its boundaries almost immediately after uploading.…”

Section: Introductionmentioning

confidence: 99%

Role of Social Networks in E-government: Risks and Security Threats

Alguliyev¹,

Alıguliyev²,

Yusifov³

2018

Online Journal of Communication and Media Technologies

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Social networks are becoming an important intermediary for interaction between governments, citizens, governmental agencies and business sectors. The popularization of social networks among users allows transforming public administration into open governance form and changing government-citizen relationships. There are various applications of social media to enable communication between users and share personal information. Currently, different attacks on social networks targeting the e-government system pose a great risk for users. In paper the role of social networks and security in egovernment is examined. Potential threats targeting the confidentiality and security of each social network user are analyzed and classified. A multi-criteria evaluation method is proposed for analysis of social networks security threats. Potential threats are ranked according to the criteria determined by the Fuzzy TOPSIS method. In the numerical study, the social network security threats are evaluated and ranked according to selected criteria (such as interception of confidential information, reputation loss in governmentcitizen (G2C) relations and organization of social-political conflicts).

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“…The problem arises when they try to influence people or spread misinformation. The importance of detecting bots in online social media has produced an active research area on this topic [9,21].…”

Section: Introduction 1motivationmentioning

confidence: 99%

Real-time Detection of Content Polluters in Partially Observable Twitter Networks

Nasim

Nguyen

Lothian³

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing real news from fake news in social media data. Identifying this type of bot is particularly challenging, with state-of-the-art methods utilising large volumes of network data as features for machine learning models. Such datasets are generally not readily available in typical applications which stream social media data for real-time event prediction. In this work we develop a methodology to detect content polluters in social media datasets that are streamed in real-time. Applying our method to the problem of civil unrest event prediction in Australia, we identify content polluters from individual tweets, without collecting social network or historical data from individual accounts. We identify some peculiar characteristics of these bots in our dataset and propose metrics for identification of such accounts. We then pose some research questions around this type of bot detection, including: how good Twitter is at detecting content polluters and how well state-of-the-art methods perform in detecting bots in our dataset. CCS CONCEPTS• Information systems → Social networking sites; • Security and privacy → Social network security and privacy; KEYWORDSCivil unrest, Social bots, Content polluters, Missing links, Twitter * Currently works at Tyto.ai † D2D CRC Stream Lead This paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

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Privacy and security in online social networks: A survey

Cited by 99 publications

References 123 publications

Predicting individuals’ vulnerability to social engineering in social networks

Predicting individuals’ vulnerability to social engineering in social networks

Role of Social Networks in E-government: Risks and Security Threats

Real-time Detection of Content Polluters in Partially Observable Twitter Networks

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