Developing a fast scam prevention mobile application: large language models

Poon, Jin Yang (2025) Developing a fast scam prevention mobile application: large language models. Final Year Project, UTAR.

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Abstract

Scams are on the rise and constantly evolving. Threat actors have abused the rise of LLM to ease the process of creating deception information for scams. Manually flagging scam information is tedious and needs to be faster to counter the rapid growth of scam cases. Therefore, this project proposes developing a "Sentinel," a real-time scam detection system leveraging a large language model (LLM) for enhanced analysis of scam audio and text messages. The strategy is to build a mobile application that automatically captures the user's text and audio input and then utilizes Google's Gemini LLM for content analysis which maximizes the flexibility of the scam detector deal with new contents smoothly. After the complete LLM analysis is ready, the application will alert the user regarding the content analysis. The project considers the importance of the user's privacy by building an active application where user content analysis will only be done upon request, ensuring that the user has full control over when and how their data is analysed. The developed application would be capable of being implemented in the majority of Android devices with minimized performance hit on lower-end smartphones. Area of Study: Cybersecurity, Artificial Intelligence Keywords: Scam Detection, Large Language Models, Real-Time Analysis, Android Application, Privacy-Preserving AI

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