Security analysis of deep neural network-based cyber-physical systems
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Kashyap, Aarti
issued: 2020-08-07T21:29:17Z
issued: 2020
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Cyber-Physical Systems (CPS) are deployed in many mission-critical applications such as medical devices (e.g., an Artificial Pancreas System (APS)), autonomous vehicular systems (e.g., self-driving cars, unmanned aerial vehicles) and aircraft control management systems (e.g., Horizontal Collision Avoidance System (HCAS) and Collision Avoidance System-Xu (ACAS-XU)). Ensuring correctness is becoming more difficult as these systems adopt new technology, such as Deep Neural Network (DNN), to control these systems. DNN are black-box algorithms whose inner workings are complex and difficult to discern. As such, understanding their vulnerabilities is also complex and difficult. We identify a new vulnerability in these systems and demonstrate how to synthesize a new category of attacks Ripple False Data Injection Attacks (RFDIA) in them by perturbing specific inputs, by minimal amounts, to stealthily change the DNN’s output. These perturbations propagate as ripples through multiple DNN layers and can lead to corruptions that can be fatal. We demonstrate that it is possible to construct such attacks efficiently by identifying the DNN’s critical inputs. The critical inputs are those that affect the final outputs the most on being perturbed. Understanding this new class of attacks sets the stage for developing methods to mitigate vulnerabilities. Our attack synthesis technique is based on modeling the attack as an optimization problem using Mixed Integer Linear Programming (MILP). We define an abstraction for DNN-based CPS that allows us to automatically: 1) identify the critical inputs, and 2) find the smallest perturbations that produce output changes. We demonstrate our technique on three practical CPS with two mission-critical applications in increasing order of complexity: Medical systems (APS) and aircraft control management systems (HCAS and ACAS-XU). Our key observations for scaling our technique to complex systems such as ACAS-XU were to define: 1) appropriate interva…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/75393
note: Security Analysis of Deep Neural Network BasedCyber-Physical SystemsbyAarti KashyapB. Technology, College of Engineering Pune, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of Applied ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Electrical and Computer Engineering)The University of British Columbia(Vancouver)August 2020© Aarti Kashyap, 2020The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Security Analysis of Deep Neural Network Based Cyber-Physical Sys-temssubmitted by Aarti Kashyap in partial fulfillment of the requirements for the de-gree of Master of Applied Science in Electrical and Computer Engineering.Examining Committee:Karthik Pattabiraman, Electrical and Computer EngineeringSupervisorMargo Seltzer, Computer ScienceCommittee memberiiAbstractCyber Physical Systems (CPS) are deployed in many mission-critical applicationssuch as medical devices (e.g., an Artifical Pancreas System (APS)), autonomousvehicular systems (e.g., self-driving cars, unmanned aerial vehicles) and aircraftcontrol management systems (e.g., Horizontal Collision Avoidance System (HCAS)and Collision Avoidance System-Xu (ACAS-XU)). Ensuring correctness is becom-ing more difficult as researchers adopt new technology, such as Deep Neural Net-work (DNN)s, to control these systems. DNNs are black box algorithms whoseinner workings are complex and difficult to discern. As such, understanding theirvulnerabilities is also complex and difficult.We identify a new vulnerability in these systems, and demonstrate how to syn-thesize a new category of attacks Ripple False Data Injection Attacks (RFDIA) inthem by perturbing specific inputs, by minimal amounts, to stealthily change theDNN’s output. These perturbations propagate as ripples through multiple DNN lay-ers and can lead to corruptions that can be fatal. We demonstrate that it is possiblet…
has type: Thesis/Dissertation
date issued: 2020-09
is shown at: 10.14288/1.0392651
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/75393