An Evidence Collection Using Blockchain for Cybercrime Detection

Inspections into cybercrime rely heavily on the use of digital evidence because of its ability to connect individuals to specific illegal activity. During a probe into a computer crime, it is essential that the integrity, authenticity, and auditability of digital evidence be maintained at all times when it is being transferred through the chain of custody from the beginning to the finish. The digitalization of banking is paralleled by an equally digitalization of the environment for financial crime. Because laws, rules, and forensic techniques are unable to keep up with the fast development of new technologies, investigations into embezzlement schemes might benefit from the standardization of processes and recording of the related approach. The applicability and adaptability of our method may be extended to include a wide variety of fraud investigations as well as routine internal audits. We offer a working Ethereum-based solution, and we incorporate standardised forensic processes and chain of custody preservation techniques. In conclusion, we investigate the challenges surrounding the mutually beneficial link between blockchain technology and financial investigations, as well as the managerial effect and potential avenues for further study. r wicked actors. In this sense, the characteristics afforded by blockchain technology, such as immutability, verifiability, and authentication, contribute to an increase in the degree of rigor that may be achieved in financial forensics. In this article, we describe not only the current status of blockchain-based digital forensic procedures but also a taxonomy of the most popular methodologies used in financial investigations. Our solution makes it possible for consumers to trace the history of their data by making use of smart contracts (CS). In conclusion, the development of an Artificial Neural Network (ANN) for blockchain makes the collection of evidence more easier. Java, which is used for clouds and blockchains, and network simulator-3.26, which is used for software-defined networking (SDN), are both used inside a single testing environment. Response time, Evidence input time, Evidence verification time, All aspects of the suggested forensic architecture, including communication overhead, hash calculation time, key generation time, encryption time, decryption time, and overall change rate, show potential.