Enhancing Digital Forensics Readiness In Big Data Wireless Medical Networks: A Secure Decentralised Framework

TL;DR

This paper introduces a secure decentralised framework to improve digital forensics readiness in Big Data wireless medical networks, addressing security threats and enhancing incident response capabilities.

Contribution

It presents a novel decentralized framework tailored for Big Data wireless medical networks, improving forensic readiness, security, and scalability compared to existing solutions.

Findings

Framework effectively improves forensic readiness and response in real-world scenarios.

Enhances resilience of medical networks against cyber threats.

Outperforms existing frameworks in scalability and decentralized data management.

Abstract

Wireless medical networks are pivotal for chronic disease management, yet the sensitive Big Data they generate presents administration challenges and cyber vulnerability. This Big Data is valuable within both healthcare and legal contexts, serving as a resource for investigating medical malpractice, civil cases, criminal activities, and network-related incidents. However, the rapid evolution of network technologies and data creates complexities in digital forensics investigations and audits. To address these issues, this paper proposes a secure decentralised framework aimed at bolstering digital forensics readiness (DFR) in Big Data wireless medical networks by identifying security threats, complexities, and gaps in current research efforts. By improving the network's resilience to cyber threats and aiding in medical malpractice investigations, this framework significantly advances digital forensics, wireless networks, and healthcare. It enhances digital forensics readiness, incident response, and the management of medical malpractice incidents in Big Data wireless medical networks. A real-world scenario-based evaluation demonstrated the framework's effectiveness in improving forensic readiness and response capabilities, validating its practical applicability and impact. A comparison of the proposed framework with existing frameworks concluded that it is an advancement in framework design for DFR, especially in regard to Big Data processing, decentralised DFR storage and scalability.