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 article presents 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. It treats time as 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.

中文翻译：

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GPLADD

对基于微电子的系统的信任可以表征为系统在系统开发过程中没有进行破坏性更改，或者系统的开发过程没有被恶意对手操纵的信心水平。在过去的十年中，对系统的信任已成为越来越受关注的问题。本文提出了一种新颖的博弈论框架，称为 GPLADD（基于图形的概率学习攻击者和动态防御者），通过分析开发时系统操作的风险，在开发过程结束时分析和量化系统可信度。GPLADD 代表一段时间内的攻击和攻击者与防御者之间的竞争。它将时间视为明确的约束，并允许将攻击者和防御者之间的信息不对称纳入分析。GPLADD 包括通过多步攻击图、攻击者和防御者策略以及玩家在不同时间的行为来明确表示攻击步骤。GPLADD 允许根据攻击者和防御者的能力和策略来量化随时间推移的攻击成功概率以及攻击者和防御者的成本。这种量化不同攻击的能力为评估开发过程中的信任提供了输入。我们在示例攻击及其变体上演示 GPLADD。我们开发了一种表示任意攻击成功概率的方法，并得出了特定攻击成功概率的显式分析特征。我们对一小组攻击进行数值蒙特卡罗研究，量化攻击成功概率、攻击者和防御者成本，