Ai driven Multiple fake account detection using deep learning techniques

The increasing prevalence of fake accounts on digital platforms poses major challenges related to identity fraud, cybercrime, and data integrity. Existing verification mechanisms, such as email registration and OTP authentication, are often bypassed using disposable emails and unregistered phone numbers. To address this issue, we propose an AI-driven system for fake account detection using Aadhaar-based identity verification combined with deep learning techniques. The system integrates Aadhaar-linked email and mobile number validation during user registration, ensuring higher authenticity. An Artificial Neural Network (ANN) is employed to classify accounts by analyzing structured identity features and behavioral patterns. The model undergoes preprocessing, normalization, and is trained to distinguish between genuine and suspicious accounts with high accuracy. Experimental results highlight the effectiveness of the proposed system in identifying multiple fake profiles with minimal false positives. This approach enhances user security and can be applied to various domains, including social media, e-commerce, and e-governance, where verified digital identity is critical.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-02
DOI
https://doi.org/10.5281/zenodo.15575328
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Ai driven Multiple fake account detection using deep learning techniques

Zenodo (CERN European Organization for Nuclear Research)
Spam and Phishing Detection
article

Ai driven Multiple fake account detection using deep learning techniques

article en

Abstract

The increasing prevalence of fake accounts on digital platforms poses major challenges related to identity fraud, cybercrime, and data integrity. Existing verification mechanisms, such as email registration and OTP authentication, are often bypassed using disposable emails and unregistered phone numbers. To address this issue, we propose an AI-driven system for fake account detection using Aadhaar-based identity verification combined with deep learning techniques. The system integrates Aadhaar-linked email and mobile number validation during user registration, ensuring higher authenticity. An Artificial Neural Network (ANN) is employed to classify accounts by analyzing structured identity features and behavioral patterns. The model undergoes preprocessing, normalization, and is trained to distinguish between genuine and suspicious accounts with high accuracy. Experimental results highlight the effectiveness of the proposed system in identifying multiple fake profiles with minimal false positives. This approach enhances user security and can be applied to various domains, including social media, e-commerce, and e-governance, where verified digital identity is critical.

Zenodo (CERN European Organization for Nuclear Research)
Vels University (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 45%
Spam and Phishing Detection
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