Publication Date

Fall 2022

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Mark Stamp

Second Advisor

Thomas Austin

Third Advisor

William Andreopoulos


Android Malware, Adverserial Attacks


Recent years have seen an increase in sales of intelligent gadgets, particularly those using the Android operating system. This popularity has not gone unnoticed by malware writers. Consequently, many research efforts have been made to develop learning models that can detect Android malware. As a countermeasure, malware writers can consider adversarial attacks that disrupt the training or usage of such learning models. In this paper, we train a wide variety of machine learning models using the KronoDroid Android malware dataset, and we consider adversarial attacks on these models. Specifically, we carefully measure the decline in performance when the feature sets used for training or testing are contaminated. Our experimental results demonstrated that elementary adversarial attacks pose a significant threat in the Android malware domain.