AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our publications.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Yang, G.; Sun, Z.; Wang, Y.
ShellBox: Adversarially Enhanced LLM-Interactive Honeypot Framework Journal Article
In: IEEE Access, vol. 13, pp. 143618–143630, 2025, ISSN: 21693536 (ISSN), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Complex networks, Dynamic error, Dynamic error simulation, Dynamics, Error simulation, Errors, honeypot, Honeypots, Interaction history, Interaction history pruning algorithm, Language Model, Large language model, Multi-turn, Network attack, Network attack and multi-turn, Network Security, Pruning algorithms, Systems analysis, Virtual Reality
@article{yang_shellbox_2025,
title = {ShellBox: Adversarially Enhanced LLM-Interactive Honeypot Framework},
author = {G. Yang and Z. Sun and Y. Wang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105013277136&doi=10.1109%2FACCESS.2025.3598779&partnerID=40&md5=f776206ec2788fc77e3766f209bc82f1},
doi = {10.1109/ACCESS.2025.3598779},
issn = {21693536 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Access},
volume = {13},
pages = {143618–143630},
abstract = {Honeypot technology is an active defence strategy designed to mitigate the asymmetry inherent in network attacks and defence dynamics. In recent years, honeypot systems powered by large language models (LLMs) have become a focal point of research owing to their ability to simulate complex network environments and generate highly deceptive virtual assets. However, response inconsistency in multi-turn dialogues and prompt injection vulnerabilities inherent to LLMs significantly reduce the deceptive capability of honeypots. This study first defines the threat model, and then introduces a relevance-based interaction history pruning algorithm and dynamic error simulation strategy to mitigate these challenges. Considering practical issues such as response latency and network instability, our experiments were conducted using multiple locally deployed open-source LLMs. The experimental results demonstrated that the proposed dynamic error simulation mechanism achieved a maximum accuracy of 81.63% for the DeepSeek-R1 model. Furthermore, applying the interaction history pruning algorithm improved the turn-level coherence score (TCS) by 34.5% compared with the baseline. Finally, this paper outlines potential future research directions for LLM-based honeypot technologies in active multi-turn mechanisms. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Complex networks, Dynamic error, Dynamic error simulation, Dynamics, Error simulation, Errors, honeypot, Honeypots, Interaction history, Interaction history pruning algorithm, Language Model, Large language model, Multi-turn, Network attack, Network attack and multi-turn, Network Security, Pruning algorithms, Systems analysis, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Honeypot technology is an active defence strategy designed to mitigate the asymmetry inherent in network attacks and defence dynamics. In recent years, honeypot systems powered by large language models (LLMs) have become a focal point of research owing to their ability to simulate complex network environments and generate highly deceptive virtual assets. However, response inconsistency in multi-turn dialogues and prompt injection vulnerabilities inherent to LLMs significantly reduce the deceptive capability of honeypots. This study first defines the threat model, and then introduces a relevance-based interaction history pruning algorithm and dynamic error simulation strategy to mitigate these challenges. Considering practical issues such as response latency and network instability, our experiments were conducted using multiple locally deployed open-source LLMs. The experimental results demonstrated that the proposed dynamic error simulation mechanism achieved a maximum accuracy of 81.63% for the DeepSeek-R1 model. Furthermore, applying the interaction history pruning algorithm improved the turn-level coherence score (TCS) by 34.5% compared with the baseline. Finally, this paper outlines potential future research directions for LLM-based honeypot technologies in active multi-turn mechanisms. © 2025 Elsevier B.V., All rights reserved.
2024
Omirgaliyev, R.; Kenzhe, D.; Mirambekov, S.
Simulating life: the application of generative agents in virtual environments Proceedings Article
In: IEEE AITU: Digit. Gener., Conf. Proc. - AITU, pp. 181–187, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 9798350364378 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Artificial intelligence agent, Artificial Intelligence Agents, Autonomous agents, Behavioral Research, Behaviour models, Computational Linguistics, Decision making, Dynamics, Dynamics simulation, Economic and social effects, Game Development, Game environment, Language Model, Large language model, large language models, Modeling languages, Social dynamic simulation, Social dynamics, Social Dynamics Simulation, Software design, Virtual Reality, Virtual Societies
@inproceedings{omirgaliyev_simulating_2024,
title = {Simulating life: the application of generative agents in virtual environments},
author = {R. Omirgaliyev and D. Kenzhe and S. Mirambekov},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199876250&doi=10.1109%2FIEEECONF61558.2024.10585387&partnerID=40&md5=3ec5256b20e46a1b275cbbe830187d6f},
doi = {10.1109/IEEECONF61558.2024.10585387},
isbn = {9798350364378 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE AITU: Digit. Gener., Conf. Proc. - AITU},
pages = {181–187},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This research explores the innovative integration of Large Language Models (LLMs) in game development, focusing on the autonomous creation, development, and governance of a virtual village by AI agents within a 2D game environment. The core of this study lies in observing and analyzing the interactions and societal development among AI agents, utilizing advanced algorithms for generative behavior modeling and dynamic skill tree learning. These AI agents are endowed with human-like decision-making capabilities, enabled by LLMs, allowing them to engage in complex social interactions and contribute to emergent societal structures within the game. The uniqueness of this project stems from its approach to simulating lifelike social dynamics in a virtual setting, thus addressing a gap in existing research and marking a significant contribution to the interdisciplinary fields of artificial intelligence and game development. By comparing AI-generated societal behaviors with human social interactions, the study delves into the potential of AI to mirror or enhance human social structures, offering a fresh perspective on the capabilities of AI in game development. This research not only aims to push the boundaries of AI applications in game development but also seeks to provide valuable insights into the potential for AI-driven simulations in studying complex social and behavioral dynamics. © 2024 Elsevier B.V., All rights reserved.},
keywords = {Artificial intelligence, Artificial intelligence agent, Artificial Intelligence Agents, Autonomous agents, Behavioral Research, Behaviour models, Computational Linguistics, Decision making, Dynamics, Dynamics simulation, Economic and social effects, Game Development, Game environment, Language Model, Large language model, large language models, Modeling languages, Social dynamic simulation, Social dynamics, Social Dynamics Simulation, Software design, Virtual Reality, Virtual Societies},
pubstate = {published},
tppubtype = {inproceedings}
}
This research explores the innovative integration of Large Language Models (LLMs) in game development, focusing on the autonomous creation, development, and governance of a virtual village by AI agents within a 2D game environment. The core of this study lies in observing and analyzing the interactions and societal development among AI agents, utilizing advanced algorithms for generative behavior modeling and dynamic skill tree learning. These AI agents are endowed with human-like decision-making capabilities, enabled by LLMs, allowing them to engage in complex social interactions and contribute to emergent societal structures within the game. The uniqueness of this project stems from its approach to simulating lifelike social dynamics in a virtual setting, thus addressing a gap in existing research and marking a significant contribution to the interdisciplinary fields of artificial intelligence and game development. By comparing AI-generated societal behaviors with human social interactions, the study delves into the potential of AI to mirror or enhance human social structures, offering a fresh perspective on the capabilities of AI in game development. This research not only aims to push the boundaries of AI applications in game development but also seeks to provide valuable insights into the potential for AI-driven simulations in studying complex social and behavioral dynamics. © 2024 Elsevier B.V., All rights reserved.
Geurts, E.; Warson, D.; Ruiz, G. Rovelo
Boosting Motivation in Sports with Data-Driven Visualizations in VR Proceedings Article
In: ACM Int. Conf. Proc. Ser., Association for Computing Machinery, 2024, ISBN: 979-840071764-2 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Asynchronoi social interaction, Asynchronous social interaction, Cycling, Data driven, Dynamics, Extended reality, Group dynamics, Language Model, Large language model, large language models, Motivation, Natural language processing systems, Real-world, Real-world data, Social interactions, Sports, User interface, User interfaces, Virtual Reality, Visualization, Visualizations
@inproceedings{geurts_boosting_2024,
title = {Boosting Motivation in Sports with Data-Driven Visualizations in VR},
author = {E. Geurts and D. Warson and G. Rovelo Ruiz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195387493&doi=10.1145%2f3656650.3656669&partnerID=40&md5=ec69e7abe61e572a94261ad6bbfed11c},
doi = {10.1145/3656650.3656669},
isbn = {979-840071764-2 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {ACM Int. Conf. Proc. Ser.},
publisher = {Association for Computing Machinery},
abstract = {In recent years, the integration of Artificial Intelligence (AI) has sparked revolutionary progress across diverse domains, with sports applications being no exception. At the same time, using real-world data sources, such as GPS, weather, and traffic data, offers opportunities to improve the overall user engagement and effectiveness of such applications. Despite the substantial advancements, including proven success in mobile applications, there remains an untapped potential in leveraging these technologies to boost motivation and enhance social group dynamics in Virtual Reality (VR) sports solutions. Our innovative approach focuses on harnessing the power of AI and real-world data to facilitate the design of such VR systems. To validate our methodology, we conducted an exploratory study involving 18 participants, evaluating our approach within the context of indoor VR cycling. By incorporating GPX files and omnidirectional video (real-world data), we recreated a lifelike cycling environment in which users can compete with simulated cyclists navigating a chosen (real-world) route. Considering the user's performance and interactions with other cyclists, our system employs AI-driven natural language processing tools to generate encouraging and competitive messages automatically. The outcome of our study reveals a positive impact on motivation, competition dynamics, and the perceived sense of group dynamics when using real performance data alongside automatically generated motivational messages. This underscores the potential of AI-driven enhancements in user interfaces to not only optimize performance but also foster a more engaging and supportive sports environment. © 2024 ACM.},
keywords = {Artificial intelligence, Asynchronoi social interaction, Asynchronous social interaction, Cycling, Data driven, Dynamics, Extended reality, Group dynamics, Language Model, Large language model, large language models, Motivation, Natural language processing systems, Real-world, Real-world data, Social interactions, Sports, User interface, User interfaces, Virtual Reality, Visualization, Visualizations},
pubstate = {published},
tppubtype = {inproceedings}
}
In recent years, the integration of Artificial Intelligence (AI) has sparked revolutionary progress across diverse domains, with sports applications being no exception. At the same time, using real-world data sources, such as GPS, weather, and traffic data, offers opportunities to improve the overall user engagement and effectiveness of such applications. Despite the substantial advancements, including proven success in mobile applications, there remains an untapped potential in leveraging these technologies to boost motivation and enhance social group dynamics in Virtual Reality (VR) sports solutions. Our innovative approach focuses on harnessing the power of AI and real-world data to facilitate the design of such VR systems. To validate our methodology, we conducted an exploratory study involving 18 participants, evaluating our approach within the context of indoor VR cycling. By incorporating GPX files and omnidirectional video (real-world data), we recreated a lifelike cycling environment in which users can compete with simulated cyclists navigating a chosen (real-world) route. Considering the user's performance and interactions with other cyclists, our system employs AI-driven natural language processing tools to generate encouraging and competitive messages automatically. The outcome of our study reveals a positive impact on motivation, competition dynamics, and the perceived sense of group dynamics when using real performance data alongside automatically generated motivational messages. This underscores the potential of AI-driven enhancements in user interfaces to not only optimize performance but also foster a more engaging and supportive sports environment. © 2024 ACM.