Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

Ismailov, M.T.; Tsikerdekis, Michail; Zeadally, Sherali

doi:10.3390/fi12090148

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…Deception-based defenses are potent weapons that have been proven to work in various domains. Their efficacy is based on the fact that they are programmed to exploit key biases to appear realistic but misleading substitutes to the hidden reality [ 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 ]. As a result, one will require a thorough understanding of both offensive and defensive trickery to implement a perfect Deception strategy.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions

Mohan

Dixit

Gyaneshwar

et al. 2022

Sensors

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With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like an attacker and determining the best strategy to counter common attack strategies. Defensive Deception tactics are beneficial at introducing uncertainty for adversaries, increasing their learning costs, and, as a result, lowering the likelihood of successful attacks. In cybersecurity, honeypots and honeytokens and camouflaging and moving target defense commonly employ Defensive Deception tactics. For a variety of purposes, deceptive and anti-deceptive technologies have been created. However, there is a critical need for a broad, comprehensive and quantitative framework that can help us deploy advanced deception technologies. Computational intelligence provides an appropriate set of tools for creating advanced deception frameworks. Computational intelligence comprises two significant families of artificial intelligence technologies: deep learning and machine learning. These strategies can be used in various situations in Defensive Deception technologies. This survey focuses on Defensive Deception tactics deployed using the help of deep learning and machine learning algorithms. Prior work has yielded insights, lessons, and limitations presented in this study. It culminates with a discussion about future directions, which helps address the important gaps in present Defensive Deception research.

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Section: Discussionmentioning

confidence: 99%

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions

Mohan

Dixit

Gyaneshwar

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The major distinctive of the privacy and security matters of the social media network is the malware detection. The nature of the malware is the spreading from the reliable sources and if the user clicks the link came from the trusted user contacts [4]. The large amount of benefits from the social media is directly proportional to the privacy issues relied on the social media network.…”

Section: Fig 2 the History Of Social Media Network Usermentioning

confidence: 99%

“…After these cyber-attacks, the management of LinkedIn started to implement a new protocol. Even after five years the same hacker "Peace" started revealing the compromised password details from the previously attacked LinkedIn account holders [4]. Since billions of social media users' credentials and personal data are circulating across the internet, social media sites must have tight protection.…”

Section: Fig 2 the History Of Social Media Network Usermentioning

confidence: 99%

A Comprehensive Study on Privacy and Security on Social Media

Albulayhi

Khediri

2022

Int. J. Interact. Mob. Technol.

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In many aspects of healthy, science, educational, functional, and social life, social media networks today are part of the human lifestyle. Social media has more impact on human life and introduced significant changes in the way people’s way of communication. People exchange a lot of information across social media networks, starting with the sharing of information with the growth of information sharing at the moment, and the advancement of technology Users create overt networks to reflect their current or new social connections. Users also upload and post a plethora of personal details. Maintaining the privacy and security of the user is a main challenge in social media. Users should feel the importance of preserving the privacy of their data and how valuable information such as banking details and confidential data should be kept away from social media. Users can also post personal information about others without their permission. The problem is exacerbated by users' lack of familiarity and knowledge, as well as the lack of appropriate resources and architecture of social media networks. This paper provides study on many privacy and security challenges encountered by social media networks and the privacy threats they pose, as well as current studies into possible solutions.

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“…"Digital racist threats" related to the sending and spreading of offensive, vulgar, and threatening messages, intended to either create false rumors or infringe someone's privacy on the net [53]. "Usurpation of the identity" linked to cybernetic identity theft acts where the victim's reputation is undermined and racist hatred is fostered towards the group the person belongs to [54].…”

Section: Instrumentmentioning

confidence: 99%

Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions

2021

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The goal of this study is to analyze how religion, ethnic group, and race influence the likelihood of becoming either a cybervictim or cyberbully in intercultural educational environments. In the research, 755 students in secondary education were analyzed in the south of Spain through the Cyberbullying Scale for students with Cultural and Religious Diversity (CSCRD). The analyses were carried out using the Statistical Package SPSS and the STATA software. The results obtained from the Kruskal–Wallis H test showed significant differences according to these aspects, for both the cybervictim and cyberbully parameters. The results stemming from binary logistic regressions confirmed such differences and regarded those students who belong to the Muslim religion, the gypsy ethnic group and the Asian race as being more likely to become cybervictims. Furthermore, these analyses showed that Gypsy and Asian students were also more likely to be cyberbullies than other groups. The main conclusions state that minority groups are more likely to suffer cyberbullying in intercultural educational environments, and that students from these groups are also more likely to become cyberbullies.

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Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

Cited by 6 publications

References 28 publications

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions

A Comprehensive Study on Privacy and Security on Social Media

Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions

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