Advances in Nonlinear Variational Analysis for Cyber Criminal Detection via Smartphone Communication Patterns

The increasing dependence on digital communication needs sophisticated ways for identifying cybercrime, particularly on mobile devices. This paper provides a comprehensive methodology that uses Natural Language Processing (NLP) tools to analyze communication patterns and detect suspicious activity. To generate communication patterns, text is preprocessed, vectorized with TF-IDF, metadata is extracted, and clustering is performed. Anomalies are identified by calculating deviations from these patterns, and suspicious signals are reported for further study. The proposed strategy outperformed baseline techniques in terms of accuracy, precision, recall, and the F1 score. Statistical study supported the method's performance, highlighting its potential as a significant tool for improving cybersecurity measures. In addition, the methodology employs Nonlinear Variational Inequalities to improve anomaly detection in communication patterns, addressing deviations more robustly within a cybersecurity framework