Enhancing Data Forensics through Edge Computing in IoT Environments
Mosope Williams, Isaac Emeteveke, Oladele J Adeyeye, Oluwatobi Emehin · International Journal of Research Publication and Reviews · 2024
The rapid expansion of Internet of Things (IoT) devices presents new opportunities and challenges in the field of digital forensics.Traditional forensic methods often rely on centralized data collection and post-event analysis, which can be inefficient for time-sensitive investigations involving vast amounts of data generated by IoT networks.This paper explores how edge computing can enhance data forensics by enabling real-time data processing closer to the source, reducing latency, and improving the speed of forensic investigations.By distributing computational tasks to the edge, investigators can analyse critical data onsite, thereby preserving the integrity of evidence and minimizing the risk of tampering during transmission.We also examine how the integration of machine learning algorithms at the edge can enhance anomaly detection, threat identification, and event correlation in IoT environments, contributing to more effective incident response.However, deploying edge computing in forensics presents its own set of challenges, particularly in securing IoT devices and ensuring that digital evidence collected from them remains trustworthy and admissible in legal contexts.This paper addresses these challenges and proposes a framework for implementing edge-based forensic investigations that prioritize data integrity, security, and efficiency.By leveraging the distributed architecture of edge computing, digital forensics in IoT environments can become more agile, accurate, and secure, paving the way for innovative forensic methodologies in smart cities, industrial IoT, and other connected ecosystems.