Researchers at Ben-Gurion University of the Negev have developed a new AI technique that will protect medical devices from malicious operating instructions in a cyberattack as well as other human and system errors.
Abnormal or anomalous instructions introduce many potentially harmful threats to patients, such as radiation overexposure, manipulation of device components or functional manipulation of medical images. Threats can occur due to cyberattacks, human errors such as a technician’s configuration mistake or host PC software bugs.
Dual-layer architecture: AI technique to protect medical devices
As part of his Ph.D. research, BGU researcher Tom Mahler has developed a technique using artificial intelligence that analyzes the instructions sent from the PC to the physical components using a new architecture for the detection of anomalous instructions.
“We developed a dual-layer architecture for the protection of medical devices from anomalous instructions,” Mahler says.
“The architecture focuses on detecting two types of anomalous instructions: (1) context-free (CF) anomalous instructions which are unlikely values or instructions such as giving 100x more radiation than typical, and (2) context-sensitive (CS) anomalous instructions, which are normal values or combinations of values, of instruction parameters, but are considered anomalous relative to a particular context, such as mismatching the intended scan type, or mismatching the patient’s age, weight, or potential diagnosis.
“For example, a normal instruction intended for an adult might be dangerous [anomalous] if applied to an infant. Such instructions may be misclassified when using only the first, CF, layer; however, by adding the second, CS, layer, they can now be detected.”
Improving anomaly detection performance
The research team evaluated the new architecture in the CT domain, using 8,277 recorded CT instructions and evaluated the CF layer using 14 different unsupervised anomaly detection algorithms. Then they evaluated the CS layer for four different types of clinical objective contexts, using five supervised classification algorithms for each context.
Adding the second CS layer to the architecture improved the overall anomaly detection performance from an F1 score of 71.6%, using only the CF layer, to between 82% and 99%, depending on the clinical objective or the body part.
Furthermore, the CS layer enables the detection of CS anomalies, using the semantics of the device’s procedure, an anomaly type that cannot be detected using only the CF layer.