• Anand Dubey
• Dr Siddhartha Choubey

ABSTRACT: Blockchain technology, a revolutionary distributed ledger system, has the potential to completely transform a range of sectors, including finance, supply chains, healthcare, and more. The decentralized nature of the block chain, however, makes it particularly challenging to identify and stop fraud. This abstract provides a summary of the state-of-the-art in research and technology for fraud detection in systems based on block chain technology. Beginning with some of the most important characteristics of blockchain, the abstract emphasizes its decentralization, transparency, and immutability. These characteristics provide a strong foundation for assuring data security and integrity, but they also pose challenging difficulties for fraud detection and prevention. The next section of the abstract looks at several fraud types that might occur in blockchain systems, such as double-spending, Sybil Identity theft, attacks, 51% attacks, and flaws in smart contracts. The potential effects of each fraud type on the reliability and integrity of blockchain systems are outlined alongside each fraud type. The abstract provides a summary of the current fraud detection methods used on blockchain systems in attempt to allay these worries. These techniques include many different approaches, including forensic analysis, consensus procedures, and machine learning algorithms, anomaly detection, and cryptographic techniques. The benefits and drawbacks of each approach are highlighted to provide a complete understanding of how it might be applied in various contexts. The lecture also addresses current fraud detection studies connected to blockchains. The fusion of blockchain technology with artificial intelligence is one of these tendencies. the development of fraud detection systems that protect user privacy, the utilization of decentralized and federated machine Utilizing data analytics and visualization tools, learning algorithms, and improving detection and investigation The conclusion of the abstract emphasizes the need of continued study and advancement in fraud detection for systems built on blockchain technology. As blockchain adoption increases across industries, it is crucial to increase the security and reliability of these systems by effectively recognizing and preventing fraud. Discussions of current obstacles and future directions for research inspire more exploration in this important area of study.

IndexTerms - Data analytics, blockchain technology, anomaly detection, decentralized ledger, machine learning, cryptography methods, privacy preservation, security, and dependability.

"Data Analytics, Privacy Protection, and Fraud Detection Security Using Blockchain And Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.h232-h243, October-2023, Available :http://www.jetir.org/papers/JETIR2310628.pdf

"Data Analytics, Privacy Protection, and Fraud Detection Security Using Blockchain And Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. pph232-h243, October-2023, Available at : http://www.jetir.org/papers/JETIR2310628.pdf