AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2025

Ahmed, Y.; Eissa, A.; Harb, O.; Miniesy, O.; Miniesy, Z.; Noureldin, M.; Mougy, A. E.

From Abstract Prompts to Cybersecurity Labs: Automating Virtual Environment Design and Deployment with Multi-Agent Systems and LLM-Driven Orchestration Proceedings Article

In: Alsmirat, M.; Alkhabbas, F.; Al-Abdullah, M.; Jararweh, Y. (Ed.): pp. 99–107, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798350392920 (ISBN).

Abstract | Links | BibTeX | Tags: Automated scenario generation, Containerization, Containers, Cybe range, cyber ranges, Cyber security, Cybersecurity, Cybersecurity training, Docker, Environment generation cybe range lab generation, environment generation cyber range lab generation, Intelligent Agents, Language Model, Large language model, large language models (LLMs), Multi agent systems, Multi-agent systems, Multiagent systems (MASs), Network Security, Personnel training, Quality assurance, Scalability, Scenarios generation, Virtual Reality

@inproceedings{ahmed_abstract_2025,

title = {From Abstract Prompts to Cybersecurity Labs: Automating Virtual Environment Design and Deployment with Multi-Agent Systems and LLM-Driven Orchestration},

author = {Y. Ahmed and A. Eissa and O. Harb and O. Miniesy and Z. Miniesy and M. Noureldin and A. E. Mougy},

editor = {M. Alsmirat and F. Alkhabbas and M. Al-Abdullah and Y. Jararweh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105016685428&doi=10.1109%2FICSC65596.2025.11140357&partnerID=40&md5=053515e92d70c5c694cc8ba888f39afa},

doi = {10.1109/ICSC65596.2025.11140357},

isbn = {9798350392920 (ISBN)},

year  = {2025},

date = {2025-01-01},

pages = {99–107},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Cyber ranges are essential for cybersecurity training, but current systems face challenges like resource-intensive infrastructures, static configurations, and laborious setups, limiting scalability and accessibility, especially for educational institutions with fewer resources. To address these issues, this paper introduces a containerized multi-agent system that automates the design and deployment of cybersecurity training environments. Using large language models (LLMs) for scenario orchestration, the system transforms natural language prompts into fully functional environments through Docker containerization. It features three specialized agents: a master agent for scenario planning, machine worker agents for environment and vulnerability generation, and a quality assurance agent for validation and debugging, ensuring modularity, scalability, and precision. Evaluated across 14 diverse attack scenarios, the system demonstrates high accuracy in generating web vulnerabilities, network exploits, and multi-step attacks. By automating scenario creation and deployment, this system enhances cybersecurity education and training, bridging critical gaps and offering a scalable, adaptive, and resource-efficient solution to meet the growing demand for skilled cybersecurity professionals. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Automated scenario generation, Containerization, Containers, Cybe range, cyber ranges, Cyber security, Cybersecurity, Cybersecurity training, Docker, Environment generation cybe range lab generation, environment generation cyber range lab generation, Intelligent Agents, Language Model, Large language model, large language models (LLMs), Multi agent systems, Multi-agent systems, Multiagent systems (MASs), Network Security, Personnel training, Quality assurance, Scalability, Scenarios generation, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

2024

Ding, P.; Liu, J.; Sun, M.; Li, L.; Liu, H.

Enhancing Computational Processing Performance for Generative AI Large Models with Autonomous Decision-Making in Metaverse Applications Proceedings Article

In: Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom, pp. 253–258, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 9798331515997 (ISBN).

Abstract | Links | BibTeX | Tags: Adversarial machine learning, AGI (Artificial General Intelligence), Artificial general intelligence, Artificial general intelligences, Autonomous decision, Autonomous Decision-Making, Data assimilation, Data integration, Decisions makings, Digital Twin Technology, Emotion Recognition, Generative adversarial networks, Generative AI large model, Generative AI Large Models, Large models, Metaverse, Metaverses, Model Acceleration, Model Compression, Multi agent systems, Multi-agent systems, Multi-modal data, Multi-Modal Data Integration, Multiagent systems (MASs), Reinforcement Learning, Reinforcement learnings, Spatio-temporal data

@inproceedings{ding_enhancing_2024,

title = {Enhancing Computational Processing Performance for Generative AI Large Models with Autonomous Decision-Making in Metaverse Applications},

author = {P. Ding and J. Liu and M. Sun and L. Li and H. Liu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211489063&doi=10.1109%2FMetaCom62920.2024.00048&partnerID=40&md5=1fed93f2068ac0087a541bd8d91a1a62},

doi = {10.1109/MetaCom62920.2024.00048},

isbn = {9798331515997 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom},

pages = {253–258},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {We explore how to enhance the computational processing performance for generative AI large models with autonomous decision-making in metaverse applications. We first introduce the relationship between AI large models and the Metaverse. We elaborate on the application scenarios of generative AI large models in Metaverse, including real-time weather simulation, embodied intelligence of agents, dynamic environment interaction, and user emotion recognition. We then propose the method of Multi-Dimensional Optimization Generation Framework (MDOGF) to improve computational processing performance. The experiment results show great improvement in computational processing performance. © 2024 Elsevier B.V., All rights reserved.},

keywords = {Adversarial machine learning, AGI (Artificial General Intelligence), Artificial general intelligence, Artificial general intelligences, Autonomous decision, Autonomous Decision-Making, Data assimilation, Data integration, Decisions makings, Digital Twin Technology, Emotion Recognition, Generative adversarial networks, Generative AI large model, Generative AI Large Models, Large models, Metaverse, Metaverses, Model Acceleration, Model Compression, Multi agent systems, Multi-agent systems, Multi-modal data, Multi-Modal Data Integration, Multiagent systems (MASs), Reinforcement Learning, Reinforcement learnings, Spatio-temporal data},

pubstate = {published},

tppubtype = {inproceedings}

}