Modeling Adversaries in a Logic for Security Protocol Analysis

TL;DR

This paper introduces a logic-based framework for modeling and analyzing security protocols under various adversary capabilities, extending beyond the traditional Dolev-Yao model to include protocol-specific knowledge and probabilistic guessing.

Contribution

It proposes a logic with algorithmic knowledge to represent diverse adversary models, enabling more flexible and realistic security protocol analysis.

Findings

Modeling of standard Dolev-Yao adversary within the logic

Extension to include protocol-specific knowledge

Incorporation of probabilistic guessing capabilities

Abstract

Logics for security protocol analysis require the formalization of an adversary model that specifies the capabilities of adversaries. A common model is the Dolev-Yao model, which considers only adversaries that can compose and replay messages, and decipher them with known keys. The Dolev-Yao model is a useful abstraction, but it suffers from some drawbacks: it cannot handle the adversary knowing protocol-specific information, and it cannot handle probabilistic notions, such as the adversary attempting to guess the keys. We show how we can analyze security protocols under different adversary models by using a logic with a notion of algorithmic knowledge. Roughly speaking, adversaries are assumed to use algorithms to compute their knowledge; adversary capabilities are captured by suitable restrictions on the algorithms used. We show how we can model the standard Dolev-Yao adversary in this setting, and how we can capture more general capabilities including protocol-specific knowledge and guesses.