Cloud and IoT Forensics for Strengthening Cybercrime Investigation: Acquisition Challenges and Analytical Frameworks

Abstract

With the rapid adoption of cloud services and Internet of Things (IoT) devices, digital forensics faces new challenges in acquiring and analyzing evidence stored across distributed and heterogeneous environments. This research presents a comparative study of forensic acquisition methods for cloud and IoT platforms, emphasizing both their potential and limitations in cybercrime investigations. Using case simulations involving Amazon Web Services (AWS), Google Cloud, and IoT-enabled smart home devices, the study evaluates logical, API-based, and memory dump acquisition strategies. Results indicate that API-driven cloud acquisition offers efficiency but faces jurisdictional restrictions, while IoT forensic acquisition remains hindered by proprietary protocols and volatile data. The paper also highlights the integration of artificial intelligence (AI) for anomaly detection, blockchain for evidence integrity, and semantic correlation frameworks to reconstruct multi-source timelines. Findings confirm that cloud and IoT forensics require hybrid technical and legal approaches to ensure evidence admissibility and investigative effectiveness.