IDENTIFYING OF FAKE PROFILES ACROSS ONLINE SOCIAL NETWORKS BY USING NUERAL NETWORK

Abstract

Online social networks have become an essential part of modern communication, connecting millions of users worldwide. However, the rapid growth of these platforms has also led to a significant increase in fake profiles, which are often created to perform malicious activities such as spreading misinformation, phishing, identity theft, and cyberattacks. Detecting such fraudulent accounts has become a critical challenge for maintaining the integrity and security of social networking platforms. This study proposes a neural network-based approach for identifying fake profiles across online social networks. The system utilizes user profile attributes, behavioral patterns, and network interactions as input features for classification. Data preprocessing and feature extraction techniques are applied to improve model performance. A supervised learning model, based on artificial neural networks, is trained to distinguish between genuine and fake accounts with high accuracy. Experimental results demonstrate that the proposed method effectively detects fake profiles and outperforms traditional machine learning techniques. The system is scalable and adaptable to different social media platforms. This approach contributes to enhancing user trust, improving cybersecurity, and reducing the risks associated with online social network misuse.