Unveiling the digital fingerprints: analysis of internet attacks based on website fingerprints

Anonymity networks are widely used to safeguard user privacy by concealing identifying metadata. However, these networks remain vulnerable to traffic analysis techniques such as website fingerprinting attacks, which can compromise user anonymity. In this study, we explore the effectiveness of several machine learning algorithms in performing such attacks. Using a controlled experimental framework, we analyse a publicly available dataset capturing user network traffic across 11 days. The dataset, recorded in .pcapng format, includes detailed traffic flows from specific web pages. Through comprehensive evaluations, we establish that the gradient boosting machine algorithm achieves the highest accuracy (83.63%) for binary classification, while random forest demonstrates superior performance (62.97% accuracy) for multi-class classification. Our analysis highlights the impact of feature engineering and algorithmic selection on classification outcomes. This work advances the understanding of privacy vulnerabilities within anonymity networks and provides insights into development of more resilient defences.