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An evolutionary algorithmic framework cloud based evidence collection architecture
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DOI：10.1007/s11042-023-14838-8.png)
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摘要
En 中文
Forensic in cloud computing is an advancement of evolutionary modern forensic science that protects against cyber criminals. Single centralize point compilation and storage of data, however, overcome the authenticity of digital evidence. In order to address this serious issue, this article suggests a evolutionary modern algorithm automated forensic platform leveraging infrastructure as a cloud service (IaaS) based on Blockchain concept. This proposed forensic structural design, evidence collection of evidence and stored on a blockchain which is circulated around several peer blocks. Secure Block Verification Mechanism (SBVM) is proposed to Safeguarding the device from unauthorised users. Using the Backtracking Search Optimization Algorithm search optimization algorithm for strengthening of the cloud environment, secret keys are optimally generated. On the bases of level of confidentiality, all data is stored and encrypted at cloud authentication server. Confidentiality-based Algebraically Homomorphic Cryptosystems learning is presented with a fast-forwarding algorithm for encryption. A block in the SDN controller is created for every data and information is stored in the cloud service provider and the history is recorded as metadata data about data. A hash based tree is constructed in each block by Secure Hash Algorithm version - 3 of 512 bits. By implementing graph theory-based graph neural networks in Smart Contracts, our framework enables users to track their data (GNNSC). Finally, the construction of a evidence graph using blockchain data enables evidence analysis. Experiments was carried out in a Python programming and blockchain integrated cloud environment with network simulator-3.30 (for Software Defined Network). As part of result our newly designed forensic architecture using blochchain (FAuB) good results in terms of evidence response time, insertion times of cloud evidence, verification time of evidence, computational overhead of evidence, hashes calculation time, keys generations times of evidence, evidence encryption time, evidence decryptions time, and total overall change rate of evidence, according to a comprehensive comparative study.
Keyword:
Evolutionary computing
Performance parameters
Fuzzy logic
Graph neural network
Blockchain technology
Evidence collection
Neural science
SHA-3
Smart contracts
Backtracking search optimization algorithm SDN control plane
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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