Leveraging Crowd Sourcing for Proficient Malevolent Users Revealing In Social Networks
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2018 by IJCTT Journal|
|Year of Publication : 2018|
|Authors : R.Madhubala,A.Prema|
|DOI : 10.14445/22312803/IJCTT-V60P111|
R.Madhubala,A.Prema "Leveraging Crowd Sourcing for Proficient Malevolent Users Revealing In Social Networks". International Journal of Computer Trends and Technology (IJCTT) V60(2):71-76 June 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
The past few years have been witnessing the theatrical popularity of large-scale social networks, where malicious nodes contact is one of the essential troubles. Most existing works focus on actively detecting malicious nodes by verifying signal relationship or actions consistency. It may not work well in large-scale social networks since the number of users is enormously large and the variation between normal users and malevolent users is unremarkable. In this paper, we recommend a novel approach that leverages the ability of users to present the discovery task. We intend motivation mechanism to persuade the contribution of users under two scenarios: Full Information and Partial Information. In full information scenario, we design a specific encouragement scheme for users according to their preferences, which can provide the desirable detection result and minimize overall cost. In partial information scenario, assuming that we only have statistical information about users, we first transform the incentive mechanism design to an optimization problem, and then design the optimal incentive scheme under different system parameters by solving the optimization problem. We perform extensive simulations to validate the analysis and demonstrate the impact of system factors on the overall cost.
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Crowd sourcing, Social Networks, Malevolent Users Revealing, Big Data.