Call for Papers
- Paper submission deadline: March 14, 2024; 11:59 PM (AoE, UTC-12)
- Acceptance notification: April 30, 2024; 11:59 PM (AoE, UTC-12)
- Camera ready due: May 15, 2024; 11:59 PM (AoE, UTC-12)
- Workshop date: July 12th, 2024
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.
This workshop solicits work that aims to advance robust 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.
Topics of Interest
Topics of interest include (but are not limited to):
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
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
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
Topics exploring methods and techniques to confidently attribute a piece of malware to its creators:
Malware Arms Race
- Binary and source-code attribution
- Adversarial attribution
Topics related to the malware arms race:
Robustness Evaluations of Malware Analysis
- 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
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
We invite the following types of papers:
Original Research papers, which are expected to be 8 pages, not exceeding 12 pages in double-column IEEE format including the 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. We acknowledge that while these elements may not be mandatory for the acceptance of a paper, they significantly contribute to enhancing the overall merit of original research, serving as valuable complements to scientific novelty.
Position or open-problem papers, of up to 6 pages, using the same template (title for this category must include the text "Position:” at the beginning). Position research papers aim at fostering discussion and collaboration by presenting preliminary research activities, work in progress and/or industrial innovations. Position research papers may summarize 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: eurosp2023-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 and properly anonymized.