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Detecting Malicious Social Bots Based On Clickstream Sequences

DETECTING MALICIOUS SOCIAL BOTS BASED ON CLICKSTREAM SEQUENCES

  • Project Code :
  • TCREJA19_85
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DETECTING MALICIOUS SOCIAL BOTS BASED ON CLICKSTREAM SEQUENCES

Detecting Malicious Social Bots Based on Clickstream Sequences

Abstract

In online social networks, social bots are social accounts controlled by automated programs that can perform corresponding operations based on a set of procedures. With the signi?cant increase in the volume, velocity, and variety of user data in online social networks, there have been attempted to design new ways of collecting and analyzing such big data. For example, social bots have been used to perform automated analytical services and provide users with improved quality of service. However, malicious social bots have also been used to disseminate false information, and this can result in real-world consequences. Therefore,  detecting and removing malicious social bots in online social networks is crucial. A novel method of detecting malicious social bots, including both features selection based on the transition probability of click stream sequences and semi-supervised clustering, is presented in this project. This method not only analyzes transition probability of user behavior click streams but also considers the time feature of behavior. Findings from our experiments on real online social network platforms demonstrate that the detection accuracy for different types of malicious social bots by the detection method of malicious social bots based on transition probability of user behavior click streams increases by an average of 12.8%, in comparison to the detection method based on quantitative analysis of user behavior.

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