ICETETI-2024 | View PDF
Paper Id:ICETETI-MSM130
doi: 10.71141/30485037/ICETETI-MSM130

Enhancing the Integrity and Security of Backup Systems Using Machine Learning and Blockchain

Ethan Davis, Muhammadu Sathik Raja Sathik Raja M.S

Citation:
Ethan Davis, Muhammadu Sathik Raja Sathik Raja M.S, "Enhancing the Integrity and Security of Backup Systems Using Machine Learning and Blockchain", International Journal of Multidisciplinary on Science and Management, pp. 227-232, 2024.

Abstract:
Data backup systems are critical for ensuring business continuity and safeguarding digital assets against data loss. However, conventional backup solutions face multiple security challenges, including unauthorized access, data tampering, ransomware attacks, and integrity verification issues. In this paper, we propose an integrated framework that leverages machine learning (ML) for anomaly detection and blockchain technology for immutable data verification. The ML model detects anomalies and potential security threats in real-time, while blockchain ensures data integrity through decentralized, tamper-proof ledgers. Our proposed system enhances security by mitigating risks associated with centralized storage, increasing transparency, and automating threat detection. We also analyze the performance, efficiency, and scalability of our approach through empirical evaluations. The results indicate that the combined use of ML and blockchain significantly improves backup system security and reliability.

Keywords: Backup security, Machine Learning, Blockchain, Data Integrity, Cybersecurity, Anomaly Detection, Encryption, Decentralized Storage.

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