Abstract
In this paper, the Grooming Attack Recognition System (GARS) is presented. The main objectives of GARS are the real-time identification, assessment and control of cyber grooming attacks in favor of child protection. The system utilizes the processes of document classification, personality recognition, user history and exposure time recording to calculate specific risks children are exposed to during chat conversations. The above processes are repeated after each new message and three of them feed corresponding fuzzy logic controllers that provide particular but homogenized risk values as outputs. The weighted sum of the particular risk values results in a total value that indicates the current cyber grooming risk the child is exposed to, as the conversation evolves. Depending on predefined thresholds, the total risk value can be used to trigger alarms for various scopes (children, parents, etc). The practical use of GARS is demonstrated with a case study based on real grooming dialogs. Furthermore, an evaluation of the proposed approach through the discussion of applicability and performance results is discussed.
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