Pushing Network Forensic Readiness to the Edge: A Resource Constrained Artificial Intelligence Based Methodology

Congratulations to Syed Rizvi and co-authors Mark Scanlon, Jimmy McGibney, and John Sheppard on the publication of Pushing Network Forensic Readiness to the Edge: A Resource Constrained Artificial Intelligence Based Methodology in 2024 Cyber Research Conference - Ireland (Cyber-RCI).

Co-authors: Mark Scanlon, Jimmy McGibney, and John Sheppard.

AI-generated summary of the contribution: The paper presents a novel approach to network forensic readiness on IoT/edge devices, addressing the challenges of limited resources and heterogeneity. The proposed NetFoREdge framework uses AI models to detect attacks, collect evidence, and preserve it for forensic analysis. The framework is evaluated on two datasets, CICIoT2023 and IoT-23, demonstrating its effectiveness in resource-constrained environments. The results show that NetFoREdge achieves high accuracy rates, reducing computational, power, and storage demands while maintaining precision and efficacy.

Read the publication.