Online Child Predator Detection Using ML
DOI:
https://doi.org/10.32628/IJSRST52310396Keywords:
ML, dataset, Training Module, Predator.Abstract
Professionals in the field need a comprehensive understanding of the risks and practices associated with online sex grooming to safeguard young individuals from online sex offenders. While the Internet offers numerous positive aspects, one of the most detrimental issues is its potential for facilitating online sexual exploitation. Originally designed as a communication tool, the Internet inadvertently provides access to promiscuous content for countless children, often in a covert manner. The objective of our task is to identify and flag potential predators through analysis of comments and online media accounts, with the intention of reporting such instances to the appropriate cyber cell administrator. Recent public surveys indicate that approximately one in five young people actively search for sexual content online each year (Finkelhor, Mitchell, & Wolak, 2000; Mitchell, Finkelhor, & Wolak, 2001). This task report outlines our progress in developing a framework to address this issue. Through the implementation of this framework, accounts associated with predatory behaviour are identified, and reports are promptly submitted to the administrator for further action.
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