University of Illinois Urbana-Champaign

Data-driven resiliency assessment of medical cyber-physical systems

Alemzadeh, Homa

Content Files
ALEMZADEH-DISSERTATION-2016.pdf

Permalink

https://hdl.handle.net/2142/90562

Description

Title
Data-driven resiliency assessment of medical cyber-physical systems
Author(s)
Alemzadeh, Homa
Issue Date
2016-04-19
Director of Research (if dissertation) or Advisor (if thesis)
Iyer, Ravishankar K.
Doctoral Committee Chair(s)
Iyer, Ravishankar K.
Committee Member(s)
  • Sanders, William H.
  • Sha, Lui R.
  • Raman, Jaishankar
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2016-07-07T19:54:08Z
Keyword(s)
  • Resiliency
  • Safety
  • Security
  • Cyber-Physical Systems
  • Medical Devices
  • Robotic Surgery
  • Food and Drug Administration (FDA)
  • Manufacturer and User Facility Device Experience (MAUDE)
  • Recalls
  • Adverse Events
  • Accident Analysis
Abstract
Advances in computing, networking, and sensing technologies have resulted in the ubiquitous deployment of medical cyber-physical systems in various clinical and personalized settings. The increasing complexity and connectivity of such systems, the tight coupling between their cyber and physical components, and the inevitable involvement of human operators in supervision and control have introduced major challenges in ensuring system reliability, safety, and security. This dissertation takes a data-driven approach to resiliency assessment of medical cyber-physical systems. Driven by large-scale studies of real safety incidents involving medical devices, we develop techniques and tools for (i) deeper understanding of incident causes and measurement of their impacts, (ii) validation of system safety mechanisms in the presence of realistic hazard scenarios, and (iii) preemptive real-time detection of safety hazards to mitigate adverse impacts on patients. We present a framework for automated analysis of structured and unstructured data from public FDA databases on medical device recalls and adverse events. This framework allows characterization of the safety issues originated from computer failures in terms of fault classes, failure modes, and recovery actions. We develop an approach for constructing ontology models that enable automated extraction of safety-related features from unstructured text. The proposed ontology model is defined based on device-specific human-in-the-loop control structures in order to facilitate the systems-theoretic causality analysis of adverse events. Our large-scale analysis of FDA data shows that medical devices are often recalled because of failure to identify all potential safety hazards, use of safety mechanisms that have not been rigorously validated, and limited capability in real-time detection and automated mitigation of hazards. To address those problems, we develop a safety hazard injection framework for experimental validation of safety mechanisms in the presence of accidental failures and malicious attacks. To reduce the test space for safety validation, this framework uses systems-theoretic accident causality models in order to identify the critical locations within the system to target software fault injection. For mitigation of safety hazards at run time, we present a model-based analysis framework that estimates the consequences of control commands sent from the software to the physical system through real-time computation of the system’s dynamics, and preemptively detects if a command is unsafe before its adverse consequences manifest in the physical system. The proposed techniques are evaluated on a real-world cyber-physical system for robot-assisted minimally invasive surgery and are shown to be more effective than existing methods in identifying system vulnerabilities and deficiencies in safety mechanisms as well as in preemptive detection of safety hazards caused by malicious attacks.
Graduation Semester
2016-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/90562
Copyright and License Information
Copyright 2016 Homa Alemzadeh

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois