Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges

Heymann, Paul; Koutrika, Georgia; García-Molina, Héctor

doi:10.1109/mic.2007.125

Cited by 221 publications

(137 citation statements)

References 4 publications

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“…Moreover, users should not reveal private information carelessly and OSN service providers should ascertain that user information is not revealed by de-anonymization attacks. The three methods suggested for dealing with spams by (Heymann et al, 2007) can be generalized for developing defense methods for social attacks: prevention, detection, and demotion. Prevention methods should guarantee that the identities belong to real people, relationships among identities are real, and messages that are posted are verified if needed.…”

Section: Forensics Analysis and Defense Methods For Social Attacksmentioning

confidence: 99%

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A Survey of Social Network Forensics

Karabiyik¹,

Canbaz²,

Aksoy³

et al. 2016

JDFSL

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Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models/techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks.

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Section: Forensics Analysis and Defense Methods For Social Attacksmentioning

confidence: 99%

“…The countermeasures for spams are grouped as detection, demotion, and prevention by (Heymann et al, 2007). These countermeasures could be generalized to all types of messages.…”

Section: Prevention Detection and Demotionmentioning

confidence: 99%

A Survey of Social Network Forensics

Karabiyik¹,

Canbaz²,

Aksoy³

et al. 2016

JDFSL

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“…Similarly, Benevenuto et al (2008) explored the attributes on video spam by applying them onto classification algorithms for video spam detection. Heymann et al (2007) summarised existing anti-spam strategies into three categories: detection-based, demotion-based, and prevention-based.…”

Section: Malicious Actor Detecting Mechanisms For Communitiesmentioning

confidence: 99%

A computational framework for detecting malicious actors in communities

Zhan

Fang

2014

IJIPSI

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Despite the significant research achievements on the study of communities, how to maintain a benign social environment for a community, as a problem, has not received much attention. Current existing malicious activity detecting mechanisms are subject to the limitation of the underlying online environment. However, we found that information plays an important role in terms of socialisation. Malicious activities, like diffusing unreliable information and providing inappropriate critics, can severely impact the situation of a community's social environment. In this paper, we propose a computational framework for detecting malicious actors in communities from the perspective of information diffusion. We use the term, 'malicious actors', to represent a group of people who intentionally or unintentionally conduct malicious behaviours to sabotage benign social environments of communities.Keywords: malicious actors; information diffusion; computational framework.Reference to this paper should be made as follows: Zhan, J. and Fang, X. (2014) 'A computational framework for detecting malicious actors in communities', Int.

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“…Heymann et al [6] classified antispam strategies into three categories: prevention, detection, and demotion. Prevention-based approaches aim at making it difficult for spam content to contribute to social tagging systems by restricting certain access types through interfaces [such as CAPTCHA [7] (which stands for "completely automated public Turing test to tell computers and humans apart") or reCAPTCHA [8]] or through usage limits (such as tagging quota, e.g., Flickr introduced a limit of 75 tags per photo [9]).…”

Section: Background and Significancementioning

confidence: 99%