Enhancing Cybersecurity: Malicious Webpage Detection Using Machine and Deep Learning

Abstract

A wide range of techniques have been proposed for detecting malicious webpages; however, with the advent of more sophisticated webpage creation processes, it has become more challenging for these approaches to deliver satisfactory outcomes. Blacklisting and classification techniques were used in the past to identify malicious webpages. The classification of the websites becomes more challenging if they are not included on the blacklist. Machine learning techniques are gaining popularity in cybersecurity. One disadvantage of the machine learning model is that it becomes slower when using content-based features. While getting the whois feature, which gives creation, updation, and expiration dates of the webpage, the webpage is physically visited. Hence, there is a chance of malicious activity. Therefore, the process of feature extraction becomes challenging and time-consuming. This article uses the Term Frequency-Inverse Document Frequency (TF-IDF) and Natural Language Processing (NLP) methods to obtain the corpus for benign and malicious words present in the Unified Resource Locator (URL). An artificial neural network (ANN) has been employed to categorize websites as benign or malicious. A comparative analysis of artificial neural networks (ANN) with other machine learning approaches has been conducted. The experimental results demonstrate that ANN has the highest accuracy of 96.70%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.