International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
AI-POWERED PHISHING DETECTION SYSTEM IN WHATSAPP WEB CLONE R B Aarthinivasini1, Sridevi S2, Subashini M3, Roshini P4, Shanjana P5 1Professor, Dept. of IT, Meenakshi College of Engineering, Tamilnadu, India
2UG Scholar, Dept. of IT, Meenakshi College of Engineering, Tamilnadu, India 3UG Scholar, Dept. of IT, Meenakshi College of Engineering, Tamilnadu, India 4UG Scholar, Dept. of IT, Meenakshi College of Engineering, Tamilnadu, India
5UG Scholar, Dept. of IT, Meenakshi College of Engineering, Tamilnadu, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Phishing is a significant cybersecurity issue,
time. Key components include BERT for text classification, OpenCV and CNN for image-based detection, and Wav2Vec for analyzing voice messages. The system ensures secure communication through AES-256 encryption and supports future enhancements for deepfake prevention and behavioural analysis.
particularly on platforms like WhatsApp, where users often receive deceptive messages, harmful links, and media files. This project focuses on designing an AI-based phishing detection tool integrated into a WhatsApp Web Clone, which actively blocks phishing threats before user interaction. Advanced ML and DL models are used in the system to examine messages, links, and visual content for threats. Technologies like Next.js, Socket.io, and PostgreSQL support the frontend, real-time communication, and backend data storage for seamless operation. The core AI module, developed in Python, utilizes OpenCV, Tesseract OCR, BERT, CNN and Google's Safe Browsing API to identify phishing patterns in shared content. Zegocloud helps enable secure and verified voice and video calls by managing authentication and encrypted transmission. The application also features voice deepfake detection using advanced audio analysis to flag potential synthetic threats. Testing in various phishing scenarios showed reliable detection accuracy and effective mitigation of fraudulent transmissions. Future enhancements include integrating deepfake detection for video and voice calls, implementing behavioral analysis, deploying AI-powered CAPTCHA, and extending the system to mobile applications beyond the WhatsApp Web clone to further strengthen security.
1.1 Aim The aim of this project is to develop an AI-powered phishing detection system integrated within a WhatsApp Web clone, capable of identifying and preventing phishing attacks in real time. By utilizing techniques from Natural Language Processing (NLP), Computer Vision (CV), this system will analyze messages, URLs, images, and deepfake voice message to detect potential phishing threats before user interaction. The goal is to enhance user security, prevent social engineering attacks, and create a safer messaging environment.
1.2 Objectives The objective of this project is to develop an AI-powered phishing detection system within a WhatsApp Web clone that can identify and prevent phishing attacks in real time before users interact with harmful content. The system will use NLP (BERT) to analyze chat messages, Google Safe Browsing API for URL detection, and OpenCV with Tesseract OCR for phishing image analysis. Additionally, the system will provide real-time alerts to notify users of potential threats while ensuring secure messaging through end-to-end encryption (AES-256). The chat application will be built using Next.js, Prisma, PostgreSQL, and WebSockets to support fast and secure communication, with future enhancements planned for voice phishing detection and AI-driven fraud prevention.
Key Words: Artificial Intelligence, Phishing Detection, WhatsApp Web, Deepfake detection, Google safe browsing API, Zego Cloud, URL detection, Image detection
1.INTRODUCTION Phishing has emerged as a serious cyber threat, especially on instant messaging platforms like WhatsApp. These platforms are frequently exploited by attackers to deceive users through fraudulent messages, disguised URLs, and misleading media content. Traditional phishing prevention strategies often fail to address real-time communication threats. This project introduces an AI-enhanced phishing detection system within a WhatsApp Web clone. The solution integrates advanced Natural Language Processing (NLP), Computer Vision (CV), and machine learning techniques to detect and block phishing attempts in real
© 2025, IRJET
|
Impact Factor value: 8.315
1.3 Purpose The primary purpose of this project is to safeguard users from phishing attacks within a messaging platform. Unlike traditional phishing detection systems that operate on emails and web browsers, this system actively prevents phishing inside real-time messaging applications. The AI-
|
ISO 9001:2008 Certified Journal
|
Page 1804