Efficient and reliable forensics using intelligent edge computing

Due to the increasing awareness and use of cloud and edge computing, society and industries are beginning to understand the benefits they can provide. Cloud and Edge are the future of information management, and they have transformed the Internet into an innovative and interactive computing platform. The ultimate goal of edge/cloud computing is to reduce the use of computing resources in the network, as well as support information sharing and intercommunication efforts within the network. Secure edge computing methodologies are applied in both open and heterogeneous network systems to protect them from many potential security threats. However, these approaches only provide passive protection for normal edge computing operations, and fail to address the security measures of several applications, particularly forensics in industrial settings. Forensics applications running on edge computing must be capable of support taking legal action against invaders for malicious damage or information theft. This paper proposes an efficient and reliable forensics framework (ERFF) to address industrial intelligent edge computing critical for the industry 4.0 implementation plan. The proposed ERFF consists of a detective module and validation model, with the detective module responsible for detecting the interaction between the client terminal and the edge resource, which means the investigator is capable of gathering the evidence securely. The security-validation model integrated with ERFF is far safer than sharing common key-based cryptographic approaches. The proposed conceptual framework is tested with Live Digital Forensic Framework for a Cloud (LDF2C), and results are compared with other existing industrial frameworks that fulfill fundamental ISO/IEC 17025 accreditation requirements, including Legal Reliable Forensic Framework (LRFF), Source Identification Network Forensics Framework (SINFF) and Logging Framework for Cloud Computing Forensic (LFCCF)). These frameworks were designed to support the digital forensic requirements of industry and academia, and experimental results validate the effectiveness of the proposed framework from reliability and efficiency perspectives as well as realistic scenarios (C) 2021 Elsevier B.V. All rights reserved.