An Intelligent Blockchain and Software-Defined Networking-Based Evidence Collection Architecture for Cloud Environment

Abstract

Cloud forensics is an extension of contemporary forensic science that guards against cybercriminals. However, consolidated data assortment and storage compromise the legitimacy of digital indication. This essay proposes an evolving modern algorithm automated forensic platform based on the blockchain idea. This proposes forensic structure design, evidence gathering, and storage on a blockchain that are peer to peer. Secure Block Verification Mechanism (SBVM) will protect unauthorised users. Secret keys are optimally produced using the cuckoo search optimization method. All data are saved and encrypted at the cloud authentication server for secrecy. Confidentiality-Based Algebraically Homomorphism, a new encryption method, is given to cryptosystem learning. Every data is assigned a block in the SDN controller, and the history is kept as metadata about data. Each block has a Secure Hash Algorithm version 3 of 512-bit hash-based tree. Our approach uses graph theory-based graph neural networks in Smart Contracts to track users’ data (GNNSC). Finally, a blockchain-based evidence graph allows for evidence analysis. The experiments were run in a cloud environment with Python and network simulator-3.30 (for software-defined network). We achieved good results in terms of evidence response time, cloud evidence insertion time, cloud evidence verification time, computational overhead, hash calculation time, key generation times, and entire overall change rate of indication using our newly deliberated forensic construction using blockchain (FAuB).