Phishing Detection Using Deep Learning for Web-Browser
Year: 2025
Type: article
Abstract: This paper suggests an end-to-end phishing detection system that includes web and email security hybrid deep learning models. For URL-based threats, URL format and page title are checked by a Char-CNN model, and a multilayer-perceptron deals with structured web attributes. In tandem, the LSTM-based approach is employed for email phishing detection, temporal and contextual extraction patterns through deep sequential learning. Both systems are deployed with scalable RESTful APIs for real-time threat classification. The shared platform emphasizes low latency, model scalability, and real-world integration into current infrastructures.
Authors: Hajira Begum, Premansh Pareek
Institution: Visvesvaraya Technological University
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410188267