- Research Article
- 10.1109/ojcs.2025.3648741
A Hybrid Deep Learning and Quantum Optimization Framework for Ransomware Response in Healthcare
- Jan 01, 2026
- IEEE Open Journal of the Computer Society
- Ahsan Ahmed + 7 more +7
Ransomware poses a growing threat to healthcare systems, compromising patient safety, operational continuity, and financial stability. Although machine learning techniques have been widely used for intrusion detection, most approaches do not support real-time, cost-sensitive response planning. In this paper, we propose a hybrid framework that integrates deep learning with quantum optimization to both predict the severity of ransomware infection and recommend optimal recovery strategies. The system employs a multilayer perceptron (MLP) trained on structured ransomware incident data to forecast infection rates, followed by a quantum post-processing module using the Quantum Approximate Optimization Algorithm (QAOA) to minimize operational and financial costs through discrete response decisions. We evaluated the framework on a realistic healthcare ransomware dataset consisting of 5,000 simulated attack scenarios. Our approach achieves a root mean squared error (RMSE) of 0.073 in the prediction of infection rates and demonstrates up to 25% cost savings over classical heuristics in recovery decisions. Extensive experiments confirm the generalizability of the model to unseen attack types, the scalability across data volumes, and the stability of the decision in risk contexts. The proposed method represents a step toward intelligent real-time ransomware mitigation systems for high-risk environments such as healthcare.
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