Strengthening Healthcare Cybersecurity by Optimizing Adversarial Attack Defences in Deep Learning Models using GPU and Parallel Processing Technologies

In healthcare, safeguarding patient data integrity is paramount, necessitating robust deep learning models. This research focuses on bolstering the cyber security defenses of these models, particularly against adversarial attacks, through the application of the Localized Adversarial Feature Attack (LAFEAT) technique. Leveraging Graphics Processing Units (GPU) and the TensorFlow framework, the study explores algorithmic enhancements like the Fast Gradient Sign Method (FGSM) and Model-Agnostic Meta-Learning (MAML). These techniques perturb input data gradients or adapt model parameters, enhancing resilience against adversarial perturbations. Additionally, Particle Swarm Optimization (PSO), a metaheuristic optimization technique, is examined to fortify defense mechanisms further. Parallel processing techniques, utilizing parallel GPU or distributed clusters, are implemented to expedite the optimization process, reducing computational burdens and enhancing scalability. Despite advancements, gaps remain in understanding how these models can be optimized for real-world healthcare applications, particularly in terms of balancing computational efficiency and robustness. The research demonstrates tangible benefits, with a significant 10% increase in success rates against adversarial attacks compared to baseline methods. Moreover, parallel processing implementation results in a 30% reduction in optimization time, improving the efficiency of cyber security defenses for deep learning models in healthcare. These numerical enhancements underscore the research’s value in fortifying healthcare systems against adversarial threats, ensuring the security and integrity of patient data while addressing existing gaps in the optimization of adversarial defenses in healthcare applications. The strengths of this research lie in its robust adversarial attack optimization techniques, integration of parallel processing for efficiency, and application to critical healthcare tasks. Comprehensive empirical results validate the significant improvements achieved in both success rates and optimization efficiency, underscoring the practical impact of this work.