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User Behavior to Identify Malicious Activities in Large-Scale Social Networks

Author(s):

Miss. Punam Ashok Bane , KIT's College of engineering,Kolhapur; Prof. S. S. Nagtilak, KIT's College of engineering,Kolhapur

Keywords:

Malicious Activity, Social Network, User Behaviors

Abstract

The enormous growth and volume of online social networks and their features, along with the vast number of socially connected users, it has become difficult to explain the true semantic value of published content for the detection of user behaviours’. Without understanding the contextual background, it is impractical to differentiate among various groups in terms of their relevance and mutual relations, or to identify the most significant representatives from the community at large. In this paper, we propose an integrated social media content analysis platform that leverages three levels of features, i.e., user-generated content, social graph connections, and user profile activities, to analyse and detect anomalous behaviours’ that deviate significantly from the norm in large-scale social networks. Several types of analyses have been conducted for a better understanding of the different user behaviours’ in the detection of highly adaptive malicious users.

Other Details

Paper ID: IJSRDV6I100086
Published in: Volume : 6, Issue : 10
Publication Date: 01/01/2019
Page(s): 92-95

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