InstaGuard: An AI-Powered Framework for Detecting Fraudulent Activities on Instagram Using Machine Learning and Deep Learning Techniques

Palani Murugan S., Gobinath R., Rahapriya K., Fahumitha Afrose B., Fathima Fazlina M., Durga Devi S.

2025

Abstract

The rapid growth of Instagram has raised major security concerns, including fake profiles, deceptive promotions, phishing scams, impersonation, and misinformation. These threats mislead users, exploit audiences, and increase cyber risks. To address these challenges, we propose InstaGuard, an AI-powered fraud detection system for Instagram. InstaGuard employs advanced AI techniques to analyze user behavior and content. Random Forest and XGBoost detect fake accounts based on profile attributes and engagement trends. BERT-based NLP models classify misleading promotions and scams, while LSTM networks and URL reputation verification identify phishing attempts. CNNs and Siamese Networks handle impersonation detection, while RoBERTa-based transformers and graph-based content analysis mitigate misinformation. By integrating Instagram’s Graph API for automated data collection, InstaGuard enables real-time fraud detection and response. Simulations indicate up to 95% accuracy, reducing false positives by 30% compared to existing methods. Its real-time capabilities ensure swift action against fraudulent activities, enhancing platform security.This research contributes to AI-driven cybersecurity by introducing a scalable, adaptive fraud detection framework tailored for Instagram. InstaGuard strengthens user safety by mitigating fraud and misinformation, reinforcing platform integrity in an increasingly complex digital landscape.

Paper Citation

in Harvard Style

S. P., R. G., K. R., B. F., M. F. and S. D. (2025). InstaGuard: An AI-Powered Framework for Detecting Fraudulent Activities on Instagram Using Machine Learning and Deep Learning Techniques. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 840-847. DOI: 10.5220/0013944600004919

in Bibtex Style

@conference{icrdicct`2525,
author={Palani S. and Gobinath R. and Rahapriya K. and Fahumitha B. and Fathima M. and Durga S.},
title={InstaGuard: An AI-Powered Framework for Detecting Fraudulent Activities on Instagram Using Machine Learning and Deep Learning Techniques},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={840-847},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013944600004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - InstaGuard: An AI-Powered Framework for Detecting Fraudulent Activities on Instagram Using Machine Learning and Deep Learning Techniques
SN - 978-989-758-777-1
AU - S. P.
AU - R. G.
AU - K. R.
AU - B. F.
AU - M. F.
AU - S. D.
PY - 2025
SP - 840
EP - 847
DO - 10.5220/0013944600004919
PB - SciTePress