Multi-modal Features Representation-based Convolutional Neural Network Model for Malicious Website Detection

Alsaedi, Mohammed; Ghaleb, Fuad A.; Saeed, Faisal; Ahmad, Jawad; Alasli, Mohammed

Abstract

Web applications have proliferated across various business sectors, serving as essential tools for billions of users in their daily lives activities. However, many of these applications are malicious which is a major threat to Internet users as they can steal sensitive information, install malware, and propagate spam. Detecting malicious websites by analyzing web content is ineffective due to the complexity of extraction of the representative features, the huge data volume, the evolving nature of the malicious patterns, the stealthy nature of the attacks, and the limitations of traditional classifiers. Uniform Resource Locators (URL) features are static and can often provide immediate insights about the website without the need to load its content. However, existing solutions for detecting malicious web applications through web content analysis often struggle due to complex feature extraction, massive data volumes, evolving attack patterns, and limitations of traditional classifiers. Leveraging solely lexical URL features proves insufficient, potentially leading to inaccurate classifications. This study proposes a multimodal representation approach that fuses textual and image-based features to enhance the performance of the malicious website detection. Textual features facilitate the deep learning model’s ability to understand and represent detailed semantic information related to attack patterns, while image features are effective in recognizing more general malicious patterns. In doing so, patterns that are hidden in textual format may be recognizable in image format. Two Convolutional Neural Network (CNN) models were constructed to extract the hidden features from both textual and image-represented features. The output layers of both models were combined and used as input for an artificial neural network classifier for decision-making. Results show the effectiveness of the proposed model when compared to other models. The overall performance in terms of Matthews...

• Journal Article Type: Article
• Acceptance Date: Dec 25, 2023
• Online Publication Date: Dec 28, 2023
• Publication Date: 2024
• Deposit Date: Jan 10, 2024
• Publicly Available Date: Jan 10, 2024
• Journal: IEEE Access
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Pages: 7271 - 7284
• DOI: https://doi.org/10.1109/access.2023.3348071
• Public URL: http://researchrepository.napier.ac.uk/Output/3440318