Towards active detection of identity clone attacks on online social networks

Jin, Lei; Takabi, Hassan; Joshi, James

doi:10.1145/1943513.1943520

Cited by 86 publications

(66 citation statements)

References 10 publications

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“…Hence, it protects only the identity of a user belonging to a given group of an OSN. In [11], the authors propose two detection schemes to discover potential faked identities in OSNs. These schemes help to resist Identity Clone Attacks (ICA), where the adversary forges the victim's identity and creates a profile with the same information and circles as friends of the victim.…”

Section: Related Workmentioning

confidence: 99%

Contrasting False Identities in Social Networks by Trust Chains and Biometric Reinforcement

Buccafurri

Lax

Migdal

et al. 2017

2017 International Conference on Cyberworlds (CW)

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Abstract-Fake identities and identity theft are issues whose relevance is increasing in the social network domain. This paper deals with this problem by proposing an innovative approach which combines a collaborative mechanism implementing a trust graph with keystroke-dynamic-recognition techniques to trust identities. The trust of each node is computed on the basis of neighborhood recognition and behavioral biometric support. The model leverages the word of mouth propagation and a settable degree of redundancy to obtain robustness. Experimental results show the benefit of the proposed solution even if attack nodes are present in the social network.

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Section: Related Workmentioning

confidence: 99%

Contrasting False Identities in Social Networks by Trust Chains and Biometric Reinforcement

Buccafurri

Lax

Migdal

et al. 2017

2017 International Conference on Cyberworlds (CW)

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“…Jin et al (2011) propose detection of faked identities based on attribute similarity in profiles and similarity of friend networks. When attacker creates a fake identity, the attacker may not only duplicate actual victim data but may also benefit from privacy settings.…”

Section: Fake Identity and Messaging Schemementioning

confidence: 99%

“…Forging identity attacks could be minimized if a person's identity is determined with respect to his/her contacts or whom he/she communicates in the network . Alternative methods may include analyzing activities of fake identities or their response to the validation process (Jin et al, 2011).…”

Section: Handling Fake Identitiesmentioning

confidence: 99%

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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“…3.1 Profile Sharing: A fascinating attribute connected with several OSNs is always to service interpersonal applications authored by third-party developers to produce added functionalities designed with top connected with users' report for OSNs [1], [5]. To supply meaningful along with beautiful solutions, these kinds of interpersonal applications ingest page capabilities, such as name, Birthday, things to do, likes and dislikes, and the like.…”

Section: Multiparty Access Control For Osns Requirements and Patmentioning

confidence: 99%

Multiparty Access Control Model for Collaborative Management of Shared Data in Online Social Networks

Anusha¹,

Murty²,

Scholar³

2014

IJIRSET

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Online systems (OSNs) have perceived astounding advancement as of late and turn into a true webpage for toxic joined with online clients. These sorts of OSNs offer excellent opportunity for computerized friendly connections and additionally subtle elements uncovering, additionally hoist various insurance and also protection concerns. Despite the fact that OSNs let clients so as to minimize utilization of imparted data, these individuals at present tend not to supply essentially any system so as to put in power security contemplations around information identified with numerous clients. To this specific end, huge numbers of us propose a system for empower the genuine protection joined with imparted insight identified with various clients in OSNs. We deliver the induction administration demonstrate so as to seize the genuine quality joined with multiparty assent details, and additionally a multiparty scope principles framework in addition to a scope implementation component. Other than, a considerable lot of us present a practical representation of our own induction administration model which we can leverage the genuine top peculiarities of late reason solvers to execute various examination obligations with your model. We besides discuss a verification of idea model of our own methodology as a major aspect of a credit application in MySpace and still give ease of use research and system assess of our own strategy.

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Towards active detection of identity clone attacks on online social networks

Cited by 86 publications

References 10 publications

Contrasting False Identities in Social Networks by Trust Chains and Biometric Reinforcement

Contrasting False Identities in Social Networks by Trust Chains and Biometric Reinforcement

A Survey of Social Network Forensics

Multiparty Access Control Model for Collaborative Management of Shared Data in Online Social Networks

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