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.

The rapid evolution of the internet, particularly the emergence of Web3, has transformed the ways people interact and share data. Web3, although still not well defined, is thought to be a return to the decentralisation of corporations' power over user data. Despite the obsolescence of the idea of building systems to detect and prevent cyber intrusions, this is still a topic of interest. This paper proposes a novel conceptual approach for implementing decentralised collaborative intrusion detection networks (CIDN) through a proof-of-concept. The study employs an analytical and comparative methodology, examining the synergy between cutting-edge Web3 technologies and information security. The proposed model incorporates blockchain concepts, cyber non-fungible token (cyberNFT) rewards, machine learning algorithms, and publish/subscribe architectures. Finally, the paper discusses the strengths and limitations of the proposed system, offering insights into the potential of decentralised cybersecurity models.