Data-Centric AI Approaches to Mitigate Cyber Threats in Connected Medical Device
Keywords:
Data-Centric AI, Cybersecurity, Connected Medical Devices, Adversarial Attacks, Infusion Pumps, False Data Injection, Real-World Clinical Data, Annotation, Dynamic Learning, FDA Guidelines, Intrusion Detection, Explainable AI, Zero-Day Exploits, Network Traffic Analysis, Model-Centric AI, Clinical Noise, Edge AI, Federated Learning, Patient Safety, Medical Device VulnerabilityAbstract
Connected medical devices, such as insulin pumps and cardiac monitors, are relied on by millions of patients, but their susceptibility to cyberattacks raises potentially lethal threats. Conventional AI-centric security frameworks pay attention to the complexity of the model, but ignore the quality of the data, which makes them brittle when confronted with clinical noise or new threats. We have introduced in this paper the need for a data-centric AI paradigm that makes dynamic learning, annotating and auditing data the frontline of defense from infiltrations. Working with [Hospital/Institution X] we created a real-world dataset of medical device network traffic augmented with adversarial threats including ransomware and false data injection. As a solution, our context-aware anomaly detection pipeline preserves clinical data by identifying anomalies in it and introduces a small and adaptive AI model that outperforms model-centric approaches by 30% in false alarm rates (F1-score 0.92 versus 0.85). Realistic case studies are presented in which simulated zero-day exploits in infusion pumps were identified without causing disruptions. Such a philosophy would directly lead to improved cybersecurity and would be consistent with various regulations such as FDA premarket guidance. Our findings highlight that, in order to safeguard medical devices, the transition needs to be from “smarter models” to “smarter data”. The addition of realistic clinical variability and contextualized, interpretable decision support assumes the provider will be in the best role to take action . Most importantly, we conclude that the security of connected medical devices is an issue of patient safety and that safety considerations must be supported by resilient, human-centered AI and grounded in quality high standards data.
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