Challenges and pitfalls in malware research. Issue 106 (July 2021) - Botacin, Marcus; Ceschin, Fabricio; Sun, Ruimin; Oliveira, Daniela; Grégio, André
Abstract: As the malware research field became more established over the last two decades, new research questions arose, such as how to make malware research reproducible, how to bring scientific rigor to attack papers, or what is an appropriate malware dataset for relevant experimental results. The challenges these questions pose also brings pitfalls that affect the multiple malware research stakeholders. To help answering those questions and to highlight potential research pitfalls to be avoided, in this paper, we present a systematic literature review of 491 papers on malware research published in major security conferences between 2000 and 2018. We identified the most common pitfalls present in past literature and propose a method for assessing current (and future) malware research. Our goal is towards integrating science and engineering best practices to develop further, improved research by learning from issues present in the published body of work. As far as we know, this is the largest literature review of its kind and the first to summarize research pitfalls in a research methodology that avoids them. In total, we discovered 20 pitfalls that limit current research impact and reproducibility. The identified pitfalls range from (i) the lack of a proper threat model, that complicates paper's evaluation, to (ii) the use of closed-source solutions and private datasets, that limit reproducibility. We also report yet-to-be-overcome challenges that are inherent to the malware nature, such as non-deterministic analysis results. Based on our findings, we propose a set of actions to be taken by the malware research and development community for future work: (i) Consolidation of malware research as a field constituted of diverse research approaches (e.g., engineering solutions, offensive research, landscapes/observational studies, and network traffic/system traces analysis); (ii) design of engineering solutions with clearer, direct assumptions (e.g., positioning solutions as proofs-of-concept vs. deployable); (iii) design of experiments that reflects (and emphasizes) the target scenario for the proposed solution (e.g., corporation, user, country-wide); (iv) clearer exposition and discussion of limitations of used technologies and exercised norms/standards for research (e.g., the use of closed-source antiviruses as ground-truth).