User behavior-based and graph-based hybrid approach for detection of Sybil Attack in online social networks

Jethava, Gordhan; Rao, Udai Pratap

doi:10.1016/j.compeleceng.2022.107753

Cited by 14 publications

(6 citation statements)

References 16 publications

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“…There are already plenty of existing works of literature that aim to detect and mitigate Sybil attacks. Jethava and Rao [17] proposed a behavior-based and graph-based approach to detect Sybil attacks in OSNs. AL-Qurishi et al [18] proposed a prediction system consisting of three modules, a data harvesting module, a feature extracting mechanism, and a deep-regression model to evaluate user-profiles and mitigate Sybil attacks on Twitter.…”

Section: Related Workmentioning

confidence: 99%

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Collusion-resistant multiparty data sharing in social networks

Shetty,

Muniyal,

Proothi

et al. 2024

IJECE

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The number of users on online social networks (OSNs) has grown tremendously over the past few years, with sites like Facebook amassing over a billion users. With the popularity of OSNs, the increase in privacy risk from the large volume of sensitive and private data is inevitable. While there are many features for access control for an individual user, most OSNs still need concrete mechanisms to preserve the privacy of data shared between multiple users. The proposed method uses metrics such as identity leakage (IL) and strength of interaction (SoI) to fine-tune the scenarios that use privacy risk and sharing loss to identify and resolve conflicts. In addition to conflict resolution, bot detection is also done to mitigate collusion attacks. The final decision to share the data item is then ascertained based on whether it passes the threshold condition for the above metrics.

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Section: Related Workmentioning

confidence: 99%

“…Conflict resolution is done after accounting for identity leakage by modifying ( 14) to incorporate identity leakage as calculated via ( 16), (17).…”

Section: Below We Show How the Proposed Model Approaches Access Contr...mentioning

confidence: 99%

Collusion-resistant multiparty data sharing in social networks

Shetty,

Muniyal,

Proothi

et al. 2024

IJECE

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“…Other Sybil detection methods include behavior-based detection methods [20], [21], [22], [23], [24], [25], [26]. They often use machine learning to classify users into benign or Sybil on the basis of their social behavior.…”

Section: Related Workmentioning

confidence: 99%

Interpreting Graph-Based Sybil Detection Methods as Low-Pass Filtering

Furutani¹,

Shibahara²,

Akiyama³

et al. 2023

IEEE Trans.Inform.Forensic Secur.

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Online social networks (OSNs) are threatened by Sybil attacks, which create fake accounts (also called Sybils) on OSNs and use them for various malicious activities. Therefore, Sybil detection is a fundamental task for OSN security. Most existing Sybil detection methods are based on the graph structure of OSNs, and various methods have been proposed recently. However, although almost all methods have been compared experimentally in terms of detection performance and noise robustness, theoretical understanding of them is still lacking. In this study, we show that existing graph-based Sybil detection methods can be interpreted in a unified framework of low-pass filtering. This framework enables us to theoretically compare and analyze each method from two perspectives: filter kernel properties and the spectrum of shift matrices. Our analysis reveals that the detection performance of each method depends on the effectiveness of the low-pass filtering. Furthermore, on the basis of the analysis, we propose a novel Sybil detection method called SybilHeat. Numerical experiments on synthetic graphs and real social networks demonstrate that SybilHeat performs consistently well on graphs with various structural properties. This study lays a theoretical foundation for graph-based Sybil detection and leads to a better understanding of Sybil detection methods.

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“…Mao et al 137 proposed a hybrid graph‐based Sybil node detection method “SybilHunter.” Based on the dynamic behavior of OSN users, it will detect Sybil accounts. Jethava and Rao 138 and Zhou and Chen 139 also used graph‐based feature to identify Sybil nodes. In Reference 140, the authors used business classification techniques and neural calculation for Sybil detection.…”

Section: Ml‐based Solutions For Osn Platformmentioning

confidence: 99%

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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User behavior-based and graph-based hybrid approach for detection of Sybil Attack in online social networks

Cited by 14 publications

References 16 publications

Collusion-resistant multiparty data sharing in social networks

Collusion-resistant multiparty data sharing in social networks

Interpreting Graph-Based Sybil Detection Methods as Low-Pass Filtering

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

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