Dynamic Routing Capsule Network Framework for Robust Phishing Website Detection

Abstract

The security landscape is increasingly at risk from phishing attackers; individuals engaging in this type of attack successfully persuade users to divulge sensitive data such as identity information or financial resources. Existing blacklist-based solutions and other approaches have proved ineffective because the adversaries are able to adjust to current bottleneck detectors and because machine learning models are based on hand-crafted behavior patterns. Deep learning brings the inductive bias of generalization to the fore while explicitly constraining the architectures to only hold local representations and to limit their capacity to express long-range relationships in data. We present a model based on CapsNet for phishing website detection in this paper. In the framework of CapsNets, dynamic routing is used to fortify the relationship between spatial and hierarchical features in a model that is built upon these orders between features. This approach is mostly to enable the model to gain complex relationships in a URL string and HTML structure. As a result of this property, the architecture is resistant to various types of adversarial perturbations like character replacement, subdomain mimicry, and misphrasing in phishing URLs. The model is evaluated the model using datasets from PhishTank, OpenPhish, and a benign domain list sourced from the Tranco list. Experimental results show that the proposed method is superior to the state-of-the-art deep learning methods and traditional classifiers in terms of accuracy, precision, recall, and ROC-AUC and reduces the false positive rate. The results confirm that CapsNet is a stable and flexible method for phishing classification that can be used for designing and implementing secure reliable web protection systems.