Using AI to Detect and Classify Suspicious Mobile Messages in Real Time
Manjunath Reddy, Rakesh Pallerla · 2025
The exponential growth of mobile messaging platforms has led to a volume of suspicious and malicious messages that pose significant risks to user privacy, financial security, and information integrity. This paper presents a novel artificial intelligence (AI)-based method for the real-time detection and classification of suspicious mobile messages. Unlike the traditional techniques, the proposed system integrates a lightweight natural language processing model with advanced machine learning algorithms to analyze content, metadata, and contextual patterns in messages. The proposed system uses a hybrid methodology integrating semantic analysis, anomaly detection, and behavioral profiling for the effective detection of such attacks. Extensive experimentation on diverse datasets highlights the robustness and scalability of the model, which achieves high precision, recall, and real-time processing capabilities without significant computational overhead. This innovative solution is well-suited for deployment in mobile applications, offering a practical and scalable framework to safeguard users against evolving threats in digital communication ecosystems.