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Title: GPLADD: Quantifying Trust in Government and Commercial Systems A Game-Theoretic Approach

Journal Article · · ACM Transactions on Privacy and Security
DOI:https://doi.org/10.1145/3326283· OSTI ID:1575267
 [1];  [1];  [1];  [2];  [1];  [3];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Georgia Inst. of Technology, Atlanta, GA (United States)
  3. The Ohio State Univ., Columbus, OH (United States)
+ Show Author Affiliations

Trust in a microelectronics-based system can be characterized as the level of confidence that a system is free of subversive alterations made during system development, or that the development process of a system has not been manipulated by a malicious adversary. Trust in systems has become an increasing concern over the past decade. This report introduces a novel game-theoretic framework, called GPLADD (Graph-based Probabilistic Learning Attacker and Dynamic Defender), for analyzing and quantifying system trustworthiness at the end of the development process, through the analysis of risk of development-time system manipulation. GPLADD represents attacks and attacker-defender contests over time. Here, time is an explicit constraint and allows incorporating the informational asymmetries between the attacker and defender into analysis. GPLADD includes an explicit representation of attack steps via multi-step attack graphs, attacker and defender strategies, and player actions at different times. GPLADD allows quantifying the attack success probability over time and the attacker and defender costs based on their capabilities and strategies. This ability to quantify different attacks provides an input for evaluation of trust in the development process. We demonstrate GPLADD on an example attack and its variants. We develop a method for representing success probability for arbitrary attacks and derive an explicit analytic characterization of success probability for a specific attack. We present a numeric Monte Carlo study of a small set of attacks, quantify attack success probabilities, attacker and defender costs, and illustrate the options the defender has for limiting the attack success and improving trust in the development process.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1575267
Report Number(s):
SAND-2019-4521J; 674919
Journal Information:
ACM Transactions on Privacy and Security, Vol. 22, Issue 3; ISSN 2471-2566
Publisher:
American Chemical Society (ACS)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 3 works
Citation information provided by
Web of Science

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Figures / Tables (16)

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Related Subjects

99 GENERAL AND MISCELLANEOUS
Trust
security
cyber security
physical security
game theory
attacker
defender
attack graphs
37 attack
stochastic process
probability theory
optimization
Deterrence
Nash equilibrium
optimal policy
PLADD
GPLADD