Paper Contents
Abstract
The extensive utilisation of IoT devices has enabled the collection of various data, however, the storage of this data poses difficulties due to the risk of corruption and undetected data integrity problems. Blockchain technology provides answers for maintaining the integrity of data. However, public blockchains might result in significant transaction expenses when dealing with massive amounts of data. Our research suggests a cost-effective and dependable method for digital forensics, which involves utilising numerous affordable blockchain networks to temporarily store data before completing the verification process on the Ethereum platform. Merkle trees hold event data in a hierarchical structure using hash functions, resulting in reduced costs for Ethereum.It is crucial to identify compromised IoT devices and gather evidence of their harmful activities. We present an innovative approach that utilises blockchain technology to collect and store digital evidence for forensic purposes. A confidential forensic evidence database holds the proof, while a permissioned blockchain ensures the evidence's security, guaranteeing its effective utilisation in legal procedures. Blockchain technology provides a practical approach for assessing unchangeable IoT records, while integrating it into IoT forensics poses financial and security obstacles.The IHBF-ML model, developed through our study, is a Cyber-Physical System (CPS) that combines Hadoop, Blockchain, and Machine Learning. It features distributed storage and is designed for forensic analysis. Utilising the Hadoop Distributed File System (HDFS) improves security in the digital realm. IHBF-ML utilises smart contracts to facilitate the transmission of IoT data and employs the Cat Boost classification approach for the purpose of anomaly detection. It employs parallel data processing using the MapReduce Framework, resulting in a 25% reduction in IoT forensic expenses compared to conventional blockchains such as Ethereum and EOS.
Copyright
Copyright © 2024 MEGHANSHI VATS. This is an open access article distributed under the Creative Commons Attribution License.
