Authors : Answer Mushedhe; Onuh Matthew Ijiga

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/muxdmepu

DOI : https://doi.org/10.38124/ijisrt/26Oct623

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The increasing migration of healthcare information systems to cloud computing environments has intensified concerns regarding database backup integrity, ransomware attacks, unauthorized data modification, backup corruption, and operational disruptions affecting the availability and recoverability of sensitive patient records. Conventional backup protection mechanisms, including checksum-based validation, scheduled backup verification, rule-based intrusion detection, and static disaster recovery procedures, provide essential safeguards but may fail to recognize sophisticated integrity attacks, identify compromised recovery points, or optimize restoration under rapidly changing threat conditions. This paper proposes BackupIntegrity, a novel artificial intelligence-enabled framework integrating adaptive anomaly detection, cryptographic integrity verification, predictive backup reliability assessment, and risk-aware automated recovery for healthcare databases hosted in cloud environments. The proposed framework introduces the Backup Integrity-Aware Adaptive Recovery Algorithm (BIARA), which combines a Temporal Transformer Network (TTN), an Isolation Forest (IF), a Gated Recurrent Unit (GRU), and a Multi-Objective Recovery Optimization (MORO) mechanism within a unified protection and restoration architecture. The Temporal Transformer captures long-range dependencies in backup activity sequences, while the Isolation Forest identifies abnormal backup access, modification, and encryption patterns. The GRU estimates backup failure probabilities and degradation trends, whereas the optimization component selects verified recovery points by jointly minimizing recovery time, potential data loss, residual compromise risk, and operational recovery costs. Cryptographic SHA-256 verification, authenticated backup manifests, immutable storage policies, and recovery-point dependency analysis provide additional safeguards against backup manipulation and contaminated restoration. The proposed system will be evaluated using a controlled cloud-based healthcare database testbed incorporating representative electronic health record transactions, incremental and full backup operations, simulated ransomware incidents, unauthorized backup modifications, storage failures, and recovery scenarios. Its performance will be compared with conventional checksum-based backup verification, rule-based security monitoring, standalone Isolation Forest, Long ShortTerm Memory (LSTM), and XGBoost-based detection and recovery approaches. Experimental evaluation will examine detection accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), falsepositive rate, backup verification latency, recovery time objective (RTO), recovery point objective (RPO), data restoration success rate, and computational overhead. Comparative performance graphs, ROC curves, recovery-time distributions, reliability trajectories, and multi-objective optimization plots will be used to determine whether BIARA provides statistically significant improvements over the benchmark approaches. The anticipated contribution is a reproducible, integrity-aware cloud recovery architecture capable of strengthening ransomware resilience, identifying trustworthy restoration points, reducing recovery uncertainty, and supporting the continuity, confidentiality, and availability of critical healthcare information systems.

Keywords : Artificial Intelligence; Healthcare Databases; Backup Integrity; Cloud Security; Disaster Recovery.

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