Security against sybil attack in social network

Shekokar, Narendra; Kansara, Krishna

doi:10.1109/icices.2016.7518887

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Shekokar et al ( 2016) [22] enriched the social graph detection by incorporating it with behavioral analysis. But, sybils could evade detection if they mimicked the behavior of honest nodes.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Sybil Guard to Detect Bots in Online Social Networks

Shetty

Muniyal

Anand

et al. 2021

JCSANDM

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Sybil accounts are swelling in popular social networking sites such as Twitter, Facebook etc. owing to cheap subscription and easy access to large masses. A malicious person creates multiple fake identities to outreach and outgrow his network. People blindly trust their online connections and fall into trap set up by these fake perpetrators. Sybil nodes exploit OSN’s ready-made connectivity to spread fake news, spamming, influencing polls, recommendations and advertisements, masquerading to get critical information, launching phishing attacks etc. Such accounts are surging in wide scale and so it has become very vital to effectively detect such nodes. In this research a new classifier (combination of Sybil Guard, Twitter engagement rate and Profile statistics analyser) is developed to combat such Sybil nodes. The proposed classifier overcomes the limitations of structure based, machine learning based and behaviour-based classifiers and is proven to be more accurate and robust than the base Sybil guard algorithm.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Sybil Guard to Detect Bots in Online Social Networks

Shetty

Muniyal

Anand

et al. 2021

JCSANDM

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“…SM characteristics, such as accessibility, flexibility, being free, and having well-connected social networks, provide users with liberty and flexibility to post and write on their platforms. Therefore, users can easily demonstrate aggressive behavior [9], [10]. SM websites have become dynamic social communication websites for millions of users worldwide.…”

Section: A Rise Of Aggressive Behavior On Smmentioning

confidence: 99%

Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges

et al. 2019

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Prior to the innovation of information communication technologies (ICT), social interactions evolved within small cultural boundaries such as geo spatial locations. The recent developments of communication technologies have considerably transcended the temporal and spatial limitations of traditional communications. These social technologies have created a revolution in user-generated information, online human networks, and rich human behavior-related data. However, the misuse of social technologies such as social media (SM) platforms, has introduced a new form of aggression and violence that occurs exclusively online. A new means of demonstrating aggressive behavior in SM websites are highlighted in this paper. The motivations for the construction of prediction models to fight aggressive behavior in SM are also outlined. We comprehensively review cyberbullying prediction models and identify the main issues related to the construction of cyberbullying prediction models in SM. This paper provides insights on the overall process for cyberbullying detection and most importantly overviews the methodology. Though data collection and feature engineering process has been elaborated, yet most of the emphasis is on feature selection algorithms and then using various machine learning algorithms for prediction of cyberbullying behaviors. Finally, the issues and challenges have been highlighted as well, which present new research directions for researchers to explore.INDEX TERMS Big data, cyberbullying, cybercrime, human aggressive behavior, machine learning, online social network, social media, text classification.

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