SafeCon: AI-Powered Real-Time Cyber Grooming Detection System - Sallahudin, Sultan; Muhammad Ismail; Ali, Subhan; Ahmed, Aftab; Faizan Hameed, Muhammad (VFAST Transactions on Software Engineering, 2025)
In recent times, the rise in online communication has unfortunately led to a significant increase in harmful activities. Countless instances involve people, especially children, becoming victims of distressing experiences like online sexual conversation. Reports suggest that a substantial number of young individuals, approximately one in four, have encountered online harassment or inappropriate content. Additionally, there has been a disturbing surge in cases involving the exploitation of children through grooming and exposure to explicit content. Leveraging the PAN12 dataset, we employ the Universal Sentence Encoder (USE) to generate text embeddings, reduce dimensionality with Principal Component Analysis (PCA), and apply K-means clustering with an optimal number of clusters determined by the Silhouette Score. This approach identifies sexually predatory conversation, enabling real-time moderation to protect users. The system also evaluates performance using a manually labeled dataset, ensuring robust detection of harmful content.