# Machine Learning Driven Smishing Detection Framework for Mobile Security

arXiv: https://arxiv.org/abs/2412.09641 · HTML: https://mlsanity.com/paper/2412.09641/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models](https://mlsanity.com/paper/2412.04756/) (2024)
- [Smart HPA: A Resource-Efficient Horizontal Pod Auto-scaler for Microservice Architectures](https://mlsanity.com/paper/2403.07909/) (2024)
- [Towards Deep Learning Enabled Cybersecurity Risk Assessment for Microservice Architectures](https://mlsanity.com/paper/2403.15169/) (2024)
- [Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion](https://mlsanity.com/paper/2310.10667/) (2023)

## Built on by (most cited first, 6 of 6)

- [RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets](https://mlsanity.com/paper/2510.14986/) (2025)
- [SCALAR: Self-Calibrating Adaptive Latent Attention Representation Learning](https://mlsanity.com/paper/2510.16474/) (2025)
- [Australian Bushfire Intelligence with AI-Driven Environmental Analytics](https://mlsanity.com/paper/2601.06105/) (2026)
- [Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.04442/) (2026)
- [AgenticVM: Agentic AI for Adaptive Software Vulnerability Management](https://mlsanity.com/paper/2605.01739/) (2026)
- [Comparative Analysis of Large Language Models in Healthcare](https://mlsanity.com/paper/2604.10316/) (2026)

## Abstract (arXiv)

The increasing reliance on smartphones for communication, financial transactions, and personal data management has made them prime targets for cyberattacks, particularly smishing, a sophisticated variant of phishing conducted via SMS. Despite the growing threat, traditional detection methods often struggle with the informal and evolving nature of SMS language, which includes abbreviations, slang, and short forms. This paper presents an enhanced content-based smishing detection framework that leverages advanced text normalization techniques to improve detection accuracy. By converting nonstandard text into its standardized form, the proposed model enhances the efficacy of machine learning classifiers, particularly the Naive Bayesian classifier, in distinguishing smishing messages from legitimate ones. Our experimental results, validated on a publicly available dataset, demonstrate a detection accuracy of 96.2%, with a low False Positive Rate of 3.87% and False Negative Rate of 2.85%. This approach significantly outperforms existing methodologies, providing a robust solution to the increasingly sophisticated threat of smishing in the mobile environment.