🤖 AI Summary
This work addresses the limitation of the classical Dolev-Yao model, which employs a binary notion of attacker knowledge and thus fails to capture the gradual accumulation of information through noisy side-channel observations. To overcome this, the authors propose a fuzzy Dolev-Yao attacker model that introduces a graded knowledge measure over the [0,1] interval and a T-norm–driven leakage update mechanism. The approach integrates explicit-state execution over a finite grid with Modified Murphi model checking, enhanced by a threshold-based security-to-failure transition scheme and concept lattice–based attribute reduction for verification efficiency. This framework reveals, for the first time, subtle vulnerabilities in classic protocols such as Needham-Schroeder-Lowe under cumulative side-channel leakage, thereby correcting longstanding misjudgments stemming from traditional binary semantics.
📝 Abstract
Classical symbolic protocol verification under Dolev--Yao uses binary attacker knowledge (known/unknown). This abstraction misses cumulative side-channel settings, where repeated noisy observations progressively improve attacker knowledge. We model this process with a graded attacker view \(μ_K\in[0,1]\), product T-norm leak updates, and finite-grid explicit-state execution in Modified Murphi.
The method is optimised with exact concept-lattice attribute reducts and exposes threshold-driven safe-to-fail transitions that are not represented in corresponding binary runs under the same bounded assumptions. Executed results on symmetric and asymmetric protocols, including Needham--Schroeder--Lowe (NSL), show that baseline models passing under crisp semantics can fail once cumulative side-channel leakage is enabled.