Holistic Cyber Security Framework for Deep Web using Federated Learning in Healthcare and Distributed Computing

There are special possibilities as well as issues for knowledge management when it comes to the Deep Web, which contains hidden and non-indexed information. In order to overcome the difficulties presented by concealed and non-indexed data, this study presents a unique framework for improving cyber security in Deep Web contexts. Federated Learning (FL) and graph-based analysis are utilised in this way. Proposed approach includes a hybrid web crawler that retrieves URLs from surface websites that are security-focused as well as illegal dark web services, enabling a thorough assessment of any risks. To ensure a thorough evaluation procedure, these URLs are evaluated using an ontology-based scoring system that compares them to predetermined reliability criteria. The findings show a significant increase in detection accuracy, with recall and precision scores more than 85% for all sources examined. These ratings are transformed into practical recommendations through the decision mapping procedure, which focuses on protecting healthcare data and stopping the unauthorised sale of personal information. This technique leverages decentralised data analysis to protect data confidentiality in cloud, fog, and edge computing settings while improving threat detection. This comprehensive approach provides a strong defence against changing cyber threats and demonstrates a flexible and dynamic approach to address modern cyber security issues.