Attack graphs are a tool for analyzing security vulnerabilities that capture different and prospective attacks on a system. As a threat modeling tool, it shows possible paths that an attacker can exploit to achieve a particular goal. However, due to the large number of vulnerabilities that are published on a daily basis, they have the potential to rapidly expand in size. Consequently, this necessitates a significant amount of resources to generate attack graphs. In addition, generating composited attack models for complex systems such as self-adaptive or AI is very difficult due to their nature to continuously change. In this paper, we present a novel fragment-based attack graph generation approach that utilizes information from publicly available information security databases. Furthermore, we also propose a domain-specific language for attack modeling, which we employ in the proposed attack graph generation approach. Finally, we present a demonstrator example showcasing the attack generator's capability to replicate a verified attack chain, as previously confirmed by security experts.

Every day, new discoveries are made by researchers from all across the globe and fields. HICSS is a flagship venue to present and discuss such scientific advances. Yet, the activities carried out for any given research can hardly be fully contained in a single document of a few pages the "paper.'' Indeed, any given study entails data, artifacts, or other material that is crucial to truly appreciate the contributions claimed in the corresponding paper. External repositories (e.g., GitHub) are a convenient tool to store all such resources so that future work can freely observe and build upon them---thereby improving transparency and promoting reproducibility of research as a whole. In this work, we scrutinize the extent to which papers recently accepted to HICSS leverage such repositories to provide supplementary material. To this end, we collect all the 5579 papers included in HICSS proceedings from 2017--2024. Then, we identify those entailing either human subject research (850) or technical implementations (737), or both (147). Finally, we review their text, examining how many include a link to an external repository and, inspect its contents. Overall, out of 2028 papers, only 3% have a functional and publicly available repository that is usable by downstream research. We release all our tools.

Large Language Models (LLMs) are transforming human decision-making by acting as cognitive collaborators. Yet, this promise comes with a paradox: while LLMs can improve accuracy, they may also erode independent reasoning, promote over-reliance and homogenize decisions. In this paper, we investigate how LLMs shape human judgment in security-critical contexts. Through two exploratory focus groups (unaided and LLM-supported), we assess decision accuracy, behavioral resilience and reliance dynamics. Our findings reveal that while LLMs enhance accuracy and consistency in routine decisions, they can inadvertently reduce cognitive diversity and improve automation bias, which is especially the case among users with lower resilience. In contrast, high-resilience individuals leverage LLMs more effectively, suggesting that cognitive traits mediate AI benefit.

The long-term sustainability of research software is a critical challenge, as it usually suffers from poor maintainability, lack of adaptability, and eventual obsolescence. This paper proposes a novel approach to addressing this issue by leveraging the concept of fitness functions from evolutionary architecture. Fitness functions are automated, continuously evaluated metrics designed to ensure that software systems meet desired non-functional, architectural qualities over time. We define a set of fitness functions tailored to the unique requirements of research software, focusing on findability, accessibility, interoperability and reusability (FAIR). These fitness functions act as proactive safeguards, promoting practices such as modular design, comprehensive documentation, version control, and compatibility with evolving technological ecosystems. By integrating these metrics into the development life cycle, we aim to foster a culture of sustainability within the research community. Case studies and experimental results demonstrate the potential of this approach to enhance the long-term FAIR of research software, bridging the gap between ephemeral project-based development and enduring scientific impact.

Attack graphs are a tool for analyzing security vulnerabilities that capture different and prospective attacks on a system. As a threat modeling tool, it shows possible paths that an attacker can exploit to achieve a particular goal. However, due to the large number of vulnerabilities that are published on a daily basis, they have the potential to rapidly expand in size. Consequently, this necessitates a significant amount of resources to generate attack graphs. In addition, generating composited attack models for complex systems such as self-adaptive or AI is very difficult due to their nature to continuously change. In this paper, we present a novel fragment-based attack graph generation approach that utilizes information from publicly available information security databases. Furthermore, we also propose a domain-specific language for attack modeling, which we employ in the proposed attack graph generation approach. Finally, we present a demonstrator example showcasing the attack generator's capability to replicate a verified attack chain, as previously confirmed by security experts.

Every day, new discoveries are made by researchers from all across the globe and fields. HICSS is a flagship venue to present and discuss such scientific advances. Yet, the activities carried out for any given research can hardly be fully contained in a single document of a few pages-the "paper." Indeed, any given study entails data, artifacts, or other material that is crucial to truly appreciate the contributions claimed in the corresponding paper. External repositories (e.g., GitHub) are a convenient tool to store all such resources so that future work can freely observe and build upon them -- thereby improving transparency and promoting reproducibility of research as a whole. In this work, we scrutinize the extent to which papers recently accepted to HICSS leverage such repositories to provide supplementary material. To this end, we collect all the 5579 papers included in HICSS proceedings from 2017-2024. Then, we identify those entailing either human subject research (850) or technical implementations (737), or both (147). Finally, we review their text, examining how many include a link to an external repository-and, inspect its contents. Overall, out of 2028 papers, only 3\% have a functional and publicly available repository that is usable by downstream research. We release all our tools.