Real Time Malicious Webpage Detection Using Hybrid CNN-LSTM Model
TL;DR
Experimental evaluation demonstrates that the Hybrid CNN–LSTM model effectively classifies webpages as Safe, Suspicious, or Malicious, providing improved detection accuracy and faster prediction compared with conventional machine learning approaches.
Abstract
The rapid growth of internet services has significantly increased the number of malicious webpages that target users through phishing, malware distribution, fake login pages, and other cyberattacks. Conventional detection techniques such as blacklist-based and signature-based methods are effective only for identifying known malicious websites and often fail to detect newly created or obfuscated URLs. To overcome these limitations, this paper presents a Real-Time Malicious Webpage Detection System using a Hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. The proposed system automatically learns structural and sequential characteristics from URL strings without relying on manually engineered features. URL preprocessing techniques including cleaning, tokenization, encoding, and normalization are performed before training the model. The CNN component extracts lexical and structural patterns from URLs, while the LSTM component captures sequential dependencies for improved classification performance. A Flask-based web application has been developed to provide real-time URL prediction through a user-friendly interface. In addition, a Google Chrome Extension has been implemented to allow users to verify webpage safety directly while browsing. Experimental evaluation demonstrates that the Hybrid CNN–LSTM model effectively classifies webpages as Safe, Suspicious, or Malicious, providing improved detection accuracy and faster prediction compared with conventional machine learning approaches. The proposed system offers a lightweight, scalable, and practical solution for enhancing web security against evolving cyber threats