Multi-Modal Deep Learning for Effective Malicious Webpage Detection

Abstract

The pervasive threat of malicious webpages, which can lead to financial loss, data breaches, and malware infections, underscores the need for effective detection methods. Conventional techniques for detecting malicious web content primarily rely on URL-based features or features extracted from various webpage components, employing a single feature vector input into a machine learning model for classifying webpages as benign or malicious. However, these approaches insufficiently address the complexities inherent in malicious webpages. To overcome this limitation, a novel Multi-Modal Deep Learning method for malicious webpage detection is proposed in this study. Three types of automatically extracted features, specifically those derived from the URL, the JavaScript code, and the webpage text, are leveraged. Each feature type is processed by a distinct deep learning model, facilitating a comprehensive analysis of the webpage. The proposed method demonstrates a high degree of effectiveness, achieving an accuracy rate of 97.90% and a false negative rate of a mere 2%. The results highlight the advantages of utilizing multimodal features and deep learning techniques for detecting malicious webpages. By considering various aspects of web content, the proposed method offers improved accuracy and a more comprehensive understanding of malicious activities, thereby enhancing web user security and effectively mitigating the risks associated with malicious webpages.