Digital Forensics Framework for Investigating Cloud-Based Cyber Crimes

The rapid adoption of cloud computing has fundamentally transformed data storage, sharing, and access mechanisms globally, offering unprecedented scalability and flexibility. However, this shift has simultaneously introduced significant new challenges for digital forensic investigators in identifying, collecting, and preserving potential evidence of cybercrimes. Traditional forensic models, which are built upon the premise of local and static data acquisition, are demonstrably unequipped to handle cloud-specific features such as multi-tenancy, dynamic resource allocation, geographic distribution of data, and complex jurisdictional boundaries. This foundational incompatibility often results in delayed investigations, compromised evidence integrity, and substantial challenges to legal admissibility in court, highlighting a critical gap between modern technological infrastructure and current investigative capabilities. This paper proposes a comprehensive Digital Forensics Framework (CDFF) specifically tailored for modern cloud-based cybercrime investigations, designed to overcome the limitations of conventional approaches. The CDFF is an end-to-end model that integrates structured processes with advanced, cloud-native technologies to ensure evidence integrity, data traceability, and legal admissibility. The framework is built around three core components: an automated evidence collection module leveraging API-based data retrieval from major cloud service providers; a robust, blockchain-based chain-of-custody ledger for the immutable recording of all handling procedures; and forensic log analytics incorporating machine learning to rapidly identify anomalous behavior within massive cloud logging streams. The framework utilizes an integrated, four-phase approach covering Identification & Authorization, Preservation & Acquisition, Analysis & Examination, and Reporting & Presentation.