Firenze: Model Evaluation Using Weak Signals

TL;DR

Firenze is a new framework that uses domain expertise encoded as scalable markers and statistical testing to evaluate machine learning models more reliably in security applications, addressing noisy and biased data issues.

Contribution

The paper introduces Firenze, a novel evaluation framework leveraging domain expertise and statistical hypothesis testing to assess ML models more robustly in security contexts.

Findings

Markers provide robust performance estimates.

Statistical testing confirms significance of differences.

Effective on malware and domain-name detection datasets.

Abstract

Data labels in the security field are frequently noisy, limited, or biased towards a subset of the population. As a result, commonplace evaluation methods such as accuracy, precision and recall metrics, or analysis of performance curves computed from labeled datasets do not provide sufficient confidence in the real-world performance of a machine learning (ML) model. This has slowed the adoption of machine learning in the field. In the industry today, we rely on domain expertise and lengthy manual evaluation to build this confidence before shipping a new model for security applications. In this paper, we introduce Firenze, a novel framework for comparative evaluation of ML models' performance using domain expertise, encoded into scalable functions called markers. We show that markers computed and combined over select subsets of samples called regions of interest can provide a robust estimate of their real-world performances. Critically, we use statistical hypothesis testing to ensure that observed differences-and therefore conclusions emerging from our framework-are more prominent than that observable from the noise alone. Using simulations and two real-world datasets for malware and domain-name-service reputation detection, we illustrate our approach's effectiveness, limitations, and insights. Taken together, we propose Firenze as a resource for fast, interpretable, and collaborative model development and evaluation by mixed teams of researchers, domain experts, and business owners.