TY - GEN
T1 - Synthesizing stealthy reprogramming attacks on cardiac devices
AU - Paoletti, Nicola
AU - Jiang, Zhihao
AU - Islam, Md Ariful
AU - Abbas, Houssam
AU - Mangharam, Rahul
AU - Lin, Shan
AU - Gruber, Zachary
AU - Smolka, Scott A.
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/4/16
Y1 - 2019/4/16
N2 - An Implantable Cardioverter Defibrillator (ICD) is a medical device used for the detection of potentially fatal cardiac arrhythmias and their treatment through the delivery of electrical shocks intended to restore normal heart rhythm. An ICD reprogramming attack seeks to alter the device’s parameters to induce unnecessary therapy or prevent required therapy. In this paper, we present a formal approach for the synthesis of ICD reprogramming attacks that are both effective, i.e., lead to fundamental changes in the required therapy, and stealthy, i.e., are hard to detect. We focus on the discrimination algorithm underlying Boston Scientific devices (one of the principal ICD manufacturers) and formulate the synthesis problem as one of multi-objective optimization. Our solution technique is based on an Optimization Modulo Theories encoding of the problem and allows us to derive device parameters that are optimal with respect to the effectiveness-stealthiness tradeoff. Our method can be tailored to the patient’s current condition, and readily generalizes to new rhythms. To the best of our knowledge, our work is the first to derive systematic ICD reprogramming attacks designed to maximize therapy disruption while minimizing detection.
AB - An Implantable Cardioverter Defibrillator (ICD) is a medical device used for the detection of potentially fatal cardiac arrhythmias and their treatment through the delivery of electrical shocks intended to restore normal heart rhythm. An ICD reprogramming attack seeks to alter the device’s parameters to induce unnecessary therapy or prevent required therapy. In this paper, we present a formal approach for the synthesis of ICD reprogramming attacks that are both effective, i.e., lead to fundamental changes in the required therapy, and stealthy, i.e., are hard to detect. We focus on the discrimination algorithm underlying Boston Scientific devices (one of the principal ICD manufacturers) and formulate the synthesis problem as one of multi-objective optimization. Our solution technique is based on an Optimization Modulo Theories encoding of the problem and allows us to derive device parameters that are optimal with respect to the effectiveness-stealthiness tradeoff. Our method can be tailored to the patient’s current condition, and readily generalizes to new rhythms. To the best of our knowledge, our work is the first to derive systematic ICD reprogramming attacks designed to maximize therapy disruption while minimizing detection.
KW - Arrhythmia discrimination
KW - Implantable Cardioverter Defibrillator
KW - Medical device security
KW - Model-based attack synthesis
KW - Reprogramming attack
UR - https://www.scopus.com/pages/publications/85066612209
U2 - 10.1145/3302509.3311044
DO - 10.1145/3302509.3311044
M3 - Conference contribution
AN - SCOPUS:85066612209
T3 - ICCPS 2019 - Proceedings of the 2019 ACM/IEEE International Conference on Cyber-Physical Systems
SP - 13
EP - 22
BT - ICCPS 2019 - Proceedings of the 2019 ACM/IEEE International Conference on Cyber-Physical Systems
A2 - Ramachandran, Gowri Sankar
A2 - Ortiz, Jorge
PB - Association for Computing Machinery, Inc
T2 - 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, part of the 2019 CPS-IoT Week
Y2 - 16 April 2019 through 18 April 2019
ER -