Blockchain and Machine Learning Approaches to Enhancing Data Privacy and Securing Distributed Systems

Syed Sadique Basha

Abstract: In modern distributed systems, ensuring data privacy and security has become increasingly challenging due to the growing sophistication of cyber threats. This paper presents a hybrid framework that integrates blockchain?s immutable and decentralized data management with machine learning?s adaptive threat detection capabilities. The proposed model leverages blockchain for secure data provenance and access control while employing machine learning algorithms to detect anomalies and prevent intrusions in real time. A layered architecture is implemented, combining cryptographic smart contracts with anomaly detection techniques to enhance security in cloud and IoT environments. Experiments using publicly available cybersecurity datasets, such as CICIDS2017 and UNSW-NB15, demonstrate improved intrusion detection accuracy and system auditability. Furthermore, the paper discusses practical implementation challenges, scalability issues, and future research directions aimed at developing intelligent, autonomous, and privacy-preserving distributed systems.

Keywords: Blockchain, Machine Learning, Distributed Systems, Cybersecurity, Intrusion Detection

How to Cite?: Syed Sadique Basha, "Blockchain and Machine Learning Approaches to Enhancing Data Privacy and Securing Distributed Systems", Volume 12 Issue 7, July 2023, International Journal of Science and Research (IJSR), Pages: 2319-2323, https://www.ijsr.net/getabstract.php?paperid=SR23721083445, DOI: https://dx.doi.org/10.21275/SR23721083445