Detection of Colluding in Online Social Networks

Online Social Networks (OSNs) are public social platforms offering users services such as connecting with remote individuals, sharing activities and experiences, and, most notably, sharing personal information. However, these platforms have their risks. OSNs have become a fundamental component in people's daily lives, not just as a platform to establish contacts but also as a means of soliciting contributions for personal data. This personal data, such as relationship status, phone numbers, address, current location, and sharing information regarding their everyday lives, opinions, political affiliations, etc., can be susceptible and should be shared cautiously. The information provided helps ingrain users in the network, driving traffic and creating treasure troves of information for hackers. This vulnerability makes OSNs a prime target for malicious users who harvest personal data to conduct attacks in the future. Users must be aware of these risks and take steps to protect their data.

OSNs have caused an exploitation of online human interaction and connectivity. Each minute, 49,380 posts are created on Instagram, and 473,400 tweets are shared on Twitter. Moreover, Facebook is one of the largest and most used OSNs, with 1.5 billion daily active users. This exchange information includes Health Conditions, User status shared on their personal account's wall such as an address, Public Individual's thoughts sharings regarding Social Establishment, educational, Personal thoughts and opinions on various aspects of social engagements, and off-course Workplace information. The user shared this information with their inner trust circle.  

Detection of Collaboration Malicious Users in Crowdsourcing: Crowdsourcing marketplaces or Internet crowdsourcing systems, such as Amazon Mechanical Turk (AMT), offer monetary rewards for completing specific tasks. As a result, they have become valuable sources of feedback for new products and services. Usually, these tasks are complex for computers to complete sentiment analysis of text, classify website content, create a 3-D photo tour, etc. This reward has begun to attract malicious users to present that behaviour; colluders are concerned with avoiding the effort usually required to produce a genuine review rather than with its quality.

The Sybil attack is a chilling example of how easy it is to exploit the vulnerabilities in OSNs. In 2012, Facebook conducted an experiment to test its network's vulnerability to fake accounts. They created numerous counterfeit accounts that appeared to be independent users and used them to manipulate the network's algorithms, spread misinformation, and influence public opinion. This demonstrated how individuals or organizations can create multiple fake accounts and use them to deceive others or gain an unfair advantage. The attackers used several techniques to exploit these vulnerabilities effectively, highlighting the potential dangers of Sybil attacks in social networks.

Mass Account Creation: The Sybil attackers created many fake accounts, making it difficult for the platform to detect and prevent their activities. This allowed them to have a significant presence on the network and amplify their reach.

Faking Relationships and Amplifying Content: The attackers established connections with legitimate users by sending friend requests, commenting on posts, and conversing. By appearing as active and engaged users, they gained the trust of others on the platform.

The attackers spread false information, rumours, and misleading content through fake accounts. This misleading content could include phoney news articles, doctored images, or deceptive advertisements. By using the network's viral nature to amplify their messages, the attackers were able to influence many users. This highlights the potential impact of misinformation and the need for user vigilance when consuming content on social networks.

In summary, the Sybil attackers exploited the vulnerabilities in Facebook's network and significantly impacted the platform's ecosystem. This example highlights the potential dangers of Sybil attacks in social networks and the importance of implementing robust security measures to prevent such malicious activities.

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