Year: 2025
Type: article
Abstract: The proliferation of fake profiles on social media and online platforms has become a growing concern, impacting user trust and the integrity of online interactions. These profiles are often used for malicious activities, such as spreading misinformation, performing fraudulent transactions, and engaging in cyberattacks. Traditional methods for detecting fake profiles rely on rule-based systems and manual verification, which are time-consuming and often ineffective. This paper explores the use of Artificial Neural Networks (ANNs) as an automated and scalable solution for identifying fake profiles. By analyzing various features such as user behavior patterns, profile information, and network connections, the proposed ANN model is trained to differentiate between legitimate and fake profiles. The model's performance is evaluated using several publicly available datasets, and results demonstrate the efficacy of ANNs in improving the accuracy and efficiency of fake profile detection compared to traditional systems.
Source: Journal of engineering sciences.
Authors: Beri Nikitha, Smt. A.Kalpana
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409237111