FRAME: Fast and Realistic Attacker Modeling and Evaluation for Temporal Logical Correlation in Static Noise

TL;DR

FRAME is a novel method that improves static noise analysis by efficiently modeling attacker behavior, reducing pessimism by over 30% without significant computational cost.

Contribution

It introduces a new approach called FRAME that exploits temporal logical correlation and novel techniques to enhance attacker modeling in static noise analysis.

Findings

Reduced pessimism by 30.4% on average in industrial designs.

Efficiently considers all relevant attackers, unlike pruning-based methods.

Achieved these improvements with minimal computational overhead.

Abstract

We propose a method called Fast and Realistic Attacker Modeling and Evaluation (FRAME) that can reduce pessimism in static noise analysis by exploiting temporal logical correlation of attackers and using novel techniques termed envelopes and $\sigma$ functions. Unlike conventional pruning-based approaches, FRAME efficiently considers all relevant attackers, thereby producing more realistic results. FRAME was tested with complex industrial design and successfully reduced the pessimism of conventional techniques by 30.4% on average, with little computational overhead.