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).
Highlights
Cloud computing is an emerging technological concept that, through virtualization technology, provides users with physical resources. e cloud computing industry is growing with the benefit of allowing network accessing to a scalable and elastic combination of shared physical or virtual resources [1] with self-owned service provisioning and ondemand available services. ere is an enhancement in the number of cloud users using cloud computing because of these features
Access to certain system layers is restricted in Software-as-aa-Service (SaaS) and Platform-as-a-a-Service (PaaS) [3] environments when the cloud environment is categorized according to the service model access to that layer which are regulated by Cloud Service Provider (CSP)
Saad Said Alkahtny developed a novel architecture to support forensic evidence collection and analysis of infrastructure as a service (IaaS) in cloud environment formally known as cloud forensic acquisition and analysis system without depending on cloud service provider and third party. is approach provides the access of deleted data and overwritten data files which is not provided in existing forensic investigation techniques [5]
Summary
Cloud computing is an emerging technological concept that, through virtualization technology, provides users with physical resources. e cloud computing industry is growing with the benefit of allowing network accessing to a scalable and elastic combination of shared physical or virtual resources [1] with self-owned service provisioning and ondemand available services. ere is an enhancement in the number of cloud users using cloud computing because of these features. Is proposed forensic architecture uses the blockchain technology to store the digital evidence and data are distributed among multiple peers. On the bases of level of confidentiality, all data are stored and encrypted at cloud authentication server. (1) In the case of cloud environment like infrastructure as a cloud service (IaaS), the digital forensics mechanism [11] design is constructed to collect, analyze, and release evidence. (2) Evidence and information are secured against malicious users by using the Secure Block Verification Mechanism (SBVM) [12] driven by a cloud authentication server (CAS). (3) Based on confidentiality level or the generation of digital signature [13] and encryption, the EL GAMAL algorithm is proposed. By using neural network-based smart contracts (GNNSCs) on graph to track data activities throughout its life cycle, the data source is preserved
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