Enhancing Social Media User’s Trust: A Comprehensive Framework for Detecting Malicious Profiles Using Multi-Dimensional Analytics

Information transparency, user privacy, and digital security are significantly vulnerable to the proliferation of counterfeit bot accounts on OSN. Traditional methods for distinguishing these accounts archetypally depend on an inadequate set of features, which hampers their effectiveness in contrast to the progressively sophisticated maneuvers employed by malicious users. A state-of-the-art methodology for Fake Bot Account Detection (FAD) that assimilates sophisticated deep learning techniques to scrutinize multimodal data, such as visual content, temporal activity patterns, and network interactions, to incredulous this challenge Visual features are analyzed using sophisticated methods, including deconstructing into smaller segments and extracting high-level patterns using encoder models. Specialized convolutions are employed to identify dependencies in user behavior over time from sequential data. By aggregating features from connected nodes and considering numerous forms of relationships, network analysis is accompanied by the social graph to learn node representations. A unified representation is created by merging these multimodal features. This representation is then transmitted through a completely associated layer and an activation function to predispose whether a bot account is genuine or counterfeit. The detection accuracy of false bot accounts is improved by integrating these diverse data modalities, which addresses the limitations of single-modality approaches. Compared with conventional methods, the FAD method is validated using the Cresci 2017 dataset, demonstrating substantial enhancements in momentous performance metrics. The consequences suggest that the proposed methodology effectively captures the multifaceted character of fake bot accounts, providing a robust tool for enhancing the security of OSNs.