  Malware, or software designed with harmful intent, is an ever-evolving threat
that can have drastic effects on both individuals and institutions. Neural
network malware classification systems are key tools for combating these
threats but are vulnerable to adversarial machine learning attacks. These
attacks perturb input data to cause misclassification, bypassing protective
systems. Existing defenses often rely on enhancing the training process,
thereby increasing the model's robustness to these perturbations, which is
quantified using verification. While training improvements are necessary, we
propose focusing on the verification process used to evaluate improvements to
training. As such, we present a case study that evaluates a novel verification
domain that will help to ensure tangible safeguards against adversaries and
provide a more reliable means of evaluating the robustness and effectiveness of
anti-malware systems. To do so, we describe malware classification and two
types of common malware datasets (feature and image datasets), demonstrate the
certified robustness accuracy of malware classifiers using the Neural Network
Verification (NNV) and Neural Network Enumeration (nnenum) tools, and outline
the challenges and future considerations necessary for the improvement and
refinement of the verification of malware classification. By evaluating this
novel domain as a case study, we hope to increase its visibility, encourage
further research and scrutiny, and ultimately enhance the resilience of digital
systems against malicious attacks.
