Call for Papers

Important Dates

• Abstract registration deadline: January 26, 2025; 11:59 PM (AoE, UTC-12)
• Paper submission deadline: January 30; 11:59 PM (AoE, UTC-12)
• Acceptance notification: March 18, 2025
• Camera ready due: April 10, 2026 (tentative)
• Workshop date: July 6, 2026

New from last year

• Original research papers are expected to have a main text of 8 pages, instead of 6 pages.

Overview

Malware research is a discipline of information security that aims to provide protection against unwanted and dangerous software. Since the mid-1980s, researchers in this area have been leading a technological arms race against creators of malware. Many ideas have been proposed, to varying degrees of effectiveness, from more traditional systems security and program analysis to the use of AI and Machine Learning. Nevertheless, with increased technological complexity and despite more sophisticated defenses, malware’s impact has grown, rather than shrunk. It appears that the defenders are continually reacting to yesterday’s threats, only to be surprised by today's minor variations. The rise of Generative AI and Large Language models opens the path for new attackers strategies at reduced costs, and complicates the work for defenders.

This lack of robustness is most apparent in signature matching, where malware is represented by a characteristic substring. The fundamental limitation of this approach is its reliance on falsifiable evidence. Mutating the characteristic substring, i.e., falsifying the evidence, is effective in evading detection, and cheaper than discovering the substring in the first place. Unsurprisingly, the same limitation applies to malware detectors based on machine learning, as long as they rely on falsifiable features for decision-making. Robust malware features are necessary. Furthermore, robust methods for malware classification and analysis are needed across the board to overcome phenomena including, but not limited to, concept drift (malware evolution), polymorphism, new malware families, new anti-analysis techniques, and adversarial machine learning, while supporting robust explanations.

WoRMA solicits work that aims to rethink how we conduct malware analysis, with the goal of creating long-term solutions to the threats of today’s digital environment. Potential research directions are malware detection, benchmark datasets, environments for malware arms race simulation, and exploring limitations of existing work, among others.

A key challenge in this field remains the gap between academia and industry, and the workshop aims to bridge this gap by fostering collaboration and knowledge exchange between both communities.

Topics of Interest

Topics of interest include (but are not limited to):

Bridging the Gap between Academia and Industry
Topics related to addressing the disconnect that often exists between academic research and its practical application in real-world industry scenarios:

• Translating research into practice
• Identifying practical constraints of theoretical models
• Applying academic rigor to real-world problems
• Releasing open data and collaborative platforms

GenAI, Large Language Models, and Malware
Topics related to the use of LLMs for both attack generation and detection, including:

• The use of Generative AI in creative ways to thwart attackers or defenders
• New risks rising from Generative AI
• Using LLMs to explain malware behaviors
• Using LLMS for better malware analysis
• Using LLMs for modifying code automatically

Malware Analysis
Topics related to understanding the malicious actions exhibited by malware:

• Identification of malware behaviors
• Identification of code modules which implement specific behaviors
• Unsupervised behavior identification
• Machine Learning and AI for behavior identification
• Reliable parsing of file formats and program code
• De-obfuscation and de-cloaking of malware
• Robust static and dynamic code analysis
• Feature extraction in presence of adversaries
• Robust signature generation and matching

Malware Detection
Topics related to techniques for malware detection:

• Developing robust malware detection, malware family recognition, identification of novel malware families
• Network-based malware analysis
• Host-based malware analysis
• Malware datasets: publication of new datasets for detection, e.g., family recognition, new family identification, behavior identification, generalization ability

Malware Attribution
Topics exploring methods and techniques to confidently attribute a piece of malware to its creators:

• Binary and source-code attribution
• Adversarial attribution

Malware Arms Race
Topics related to the malware arms race:

• Virtual malware arms race environments and competition reports – automated bots of malware and detectors simultaneously attacking and defending networked hosts, adaptively co-evolving in their quest towards supremacy
• Automated countermeasures to malware anti-analysis techniques, e.g., packing, anti-debugging, anti-emulation
• Bypassing anti-malware (anti-virus), e.g., via problem-space adversarial modifications

Robustness Evaluations of Malware Analysis
Topics exploring the limitations of existing research:

• Experiments demonstrating the limitations in robustness of existing methods (for detection, unpacking, behavior analysis, etc.), datasets, defenses
• Machine learning-based malware analysis and adversarial machine learning
• Overcoming limitations – demonstrating methods resilient to, e.g., concept drift (malware evolution), polymorphism, new malware families, new anti-analysis techniques, or adversarial machine learning defenses

Submission Guidelines

We invite the following types of papers:

• Original Research papers. Main body of up to 8 pages, not exceeding 12 pages in double-column IEEE EuroS&P 2026 format with references and appendices. This category of papers should describe original work that is not previously published or concurrently submitted elsewhere. In this category, we strongly encourage the submission of open-source software artifacts and emphasize the importance of reproducibility in results. While these elements are not mandatory for acceptance, they significantly contribute to enhancing the overall merit of original research.
• Position or open-problem papers. Main body of up to 6 pages, using the same template, not exceeding 10 pages with references and appendices. The title for this category must include the text Position: at the beginning. Position papers aim to foster discussion and collaboration by presenting preliminary research activities, work in progress, and/or industrial innovations. These papers may also systematize research results published elsewhere or outline new emerging ideas.

Submissions must be anonymous (double-blind review), and authors should refer to their previous work in the third person. Submissions must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or conference with proceedings.

Papers must be typeset in LaTeX in A4 format (not "US Letter") using the IEEE conference proceeding template supplied by IEEE EuroS&P: eurosp-template.zip. Please do not use other IEEE templates.

Submissions must be in Portable Document Format (.pdf). Authors should pay special attention to unusual fonts, images, and figures that might create problems for reviewers. Your document should render correctly in Adobe Reader XI and when printed in black and white.

Accepted papers will be published in IEEE Xplore. One author of each accepted paper is required to attend the workshop and present the paper for it to be included in the proceedings. Committee members are not required to read the appendices, so the paper should be intelligible without them. Submissions must be in English.

Use of Generative AI tools

Guidelines for Authors: We encourage authors to use any suitable tools, including Large Language Models (LLMs), for preparing high-quality papers. However, authors must adhere to three key criteria:

• The methodology of use must be thoroughly documented and reported as citations or footnotes.
• If LLMs are integral to the contributions of the paper, their use should be explicitly motivated and justified in the body of the paper.
• The authors are responsible for the entire content of their paper, including all text and figures. While any tool may be used for improving writing and presentation (with due documentation), it is crucial that all content is accurate (i.e., factually correct and aligned with the research presented) and original (i.e., avoids plagiarism), ensuring transparency and maintaining the integrity of the research process.

Guidelines for Reviewers: To protect the nature of the peer-review process, reviewers are expected to form their opinion about the paper and construct their feedback independently, i.e., without applying any automated analysis or reasoning tools to the workshop submissions. The reviewers are strictly disallowed to input the paper PDFs and text snippets from the reviewed paper into any AI-based tool, including LLMs and AI detection tools.

Submission Site

HotCrp Submission Website: TBA