Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Elhajjout, A.; Jarir, Z.; Moudoud, H.; Davoust, A.; Houda, Z. A. El
Leveraging Large Language Models for Contextual Threat Hypothesis Generation in IoT Networks Proceedings Article
In: Dig Tech Pap IEEE Int Conf Consum Electron, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 0747668X (ISSN); 979-833155343-2 (ISBN), (Journal Abbreviation: Dig Tech Pap IEEE Int Conf Consum Electron).
Abstract | Links | BibTeX | Tags: Alert Triage, Cybersecurity, Hypotheses generation, Internet of thing security, Internet of things, IoT Security, Language model, Large datasets, Large language model, large language models, Network security, Prompt Engineering, Security alerts, Security operation center, Security Operations, Security systems, Threat Hypothesis Generation
@inproceedings{elhajjoutLeveragingLargeLanguage2026,
title = {Leveraging Large Language Models for Contextual Threat Hypothesis Generation in IoT Networks},
author = {A. Elhajjout and Z. Jarir and H. Moudoud and A. Davoust and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105037351870?origin=resultslist},
doi = {10.1109/ICCE67443.2026.11449914},
isbn = {0747668X (ISSN); 979-833155343-2 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Dig Tech Pap IEEE Int Conf Consum Electron},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Modern Security Operations Centers face numerous challenges in managing the volume and complexity of security alerts from Internet of Things (IoT) networks. While traditional rule-based systems excel at detection, they provide limited contextual reasoning to help analysts understand ambiguous alerts. Large Language Models (LLMs) have shown promise across various cybersecurity domains. However, their potential to generate rich, explanatory threat hypotheses for ambiguous IoT alerts remains largely unexplored. To address these issues, in this paper, we present a systematic investigation of Large Language Model-based threat hypothesis generation with three key contributions. First, we introduce a context-aware structured prompting framework capable of synthesizing heterogeneous device telemetry into coherent threat narratives. Second, we formalize the assessment of explainability in security through a rigorous multidimensional quality metric system. Third, we provide the first comparative analysis of five state-of-the-art models on real-world IoT incidents, revealing critical performance tradeoffs. Results show that GPT-4o-mini achieves 88.2% accuracy on diverse attack types, while Qwen 3-235B achieves 95.2% accuracy on network-focused attacks. Statistical analysis reveals significant model-dataset interactions. These findings show that properly guided Large Language Models can augment analyst capabilities by providing detailed, contextual explanations that facilitate efficient alert triage. © 2026 IEEE.},
note = {Journal Abbreviation: Dig Tech Pap IEEE Int Conf Consum Electron},
keywords = {Alert Triage, Cybersecurity, Hypotheses generation, Internet of thing security, Internet of things, IoT Security, Language model, Large datasets, Large language model, large language models, Network security, Prompt Engineering, Security alerts, Security operation center, Security Operations, Security systems, Threat Hypothesis Generation},
pubstate = {published},
tppubtype = {inproceedings}
}
Modern Security Operations Centers face numerous challenges in managing the volume and complexity of security alerts from Internet of Things (IoT) networks. While traditional rule-based systems excel at detection, they provide limited contextual reasoning to help analysts understand ambiguous alerts. Large Language Models (LLMs) have shown promise across various cybersecurity domains. However, their potential to generate rich, explanatory threat hypotheses for ambiguous IoT alerts remains largely unexplored. To address these issues, in this paper, we present a systematic investigation of Large Language Model-based threat hypothesis generation with three key contributions. First, we introduce a context-aware structured prompting framework capable of synthesizing heterogeneous device telemetry into coherent threat narratives. Second, we formalize the assessment of explainability in security through a rigorous multidimensional quality metric system. Third, we provide the first comparative analysis of five state-of-the-art models on real-world IoT incidents, revealing critical performance tradeoffs. Results show that GPT-4o-mini achieves 88.2% accuracy on diverse attack types, while Qwen 3-235B achieves 95.2% accuracy on network-focused attacks. Statistical analysis reveals significant model-dataset interactions. These findings show that properly guided Large Language Models can augment analyst capabilities by providing detailed, contextual explanations that facilitate efficient alert triage. © 2026 IEEE.
2.
Malasi, J. -J. M.; Moudoud, H.; Missaoui, R.
A Lightweight Multimodal LLM-Based Intrusion Detection System for Open RAN Proceedings Article
In: IEEE Wirel. Commun. Netw. Conf. Workshops, WCNCW, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833157731-5 (ISBN), (Journal Abbreviation: IEEE Wirel. Commun. Netw. Conf. Workshops, WCNCW).
Abstract | Links | BibTeX | Tags: Benchmarking, Computational linguistics, Computer crime, Cyber security, Cybersecurity, Embedded systems, Embeddings, Intelligent controllers, Internet protocols, Intrusion Detection, intrusion detection system, Intrusion Detection System (IDS), Intrusion Detection Systems, Language model, Large language model, Large Language Models (LLMs), Mobile telecommunication systems, Multi-modal, Multi-modal learning, Multimodal Learning, Network security, Open RAN, Pipelines, RAN intelligent controller, RAN intelligent controller (RIC), Semantics
@inproceedings{malasiLightweightMultimodalLLMBased2026,
title = {A Lightweight Multimodal LLM-Based Intrusion Detection System for Open RAN},
author = {J. -J. M. Malasi and H. Moudoud and R. Missaoui},
url = {https://www.scopus.com/pages/publications/105043415034?origin=resultslist},
doi = {10.1109/WCNCW67598.2026.11555393},
isbn = {979-833157731-5 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Wirel. Commun. Netw. Conf. Workshops, WCNCW},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Open Radio Access Network architectures introduce unprecedented openness and programmability through RAN Intelligent Controllers, expanding the attack surface beyond traditional volumetric threats. Most existing intrusion detection systems for fifth-generation mobile networks and ORAN rely primarily on numerical Key Performance Indicators (KPIs), often overlooking the security-relevant semantics embedded in logs and control messages. This paper proposes LLM4IDS, a semantic-aware and multimodal intrusion detection system that fuses lightweight Large Language Model (LLM) embeddings of textified events with traditional tabular KPIs in a compact Transformer-based classifier. Evaluated on the realworld OpenIreland O-RAN dataset and two classical IP network benchmarks (UNSW-NB15 and CICIDS2017), LLM4IDS consistently matches or surpasses strong tabular baselines while maintaining high efficiency. On OpenIreland, it reaches nearperfect detection (F1-score $textbackslashapprox 1.0$) and yields large gains for application-layer and volumetric attacks compared to KPI-only models. Across datasets, the classifier maintains an approximately 4 MB footprint and sub-millisecond CPU inference latency; the frozen sentence-LLM encoder can be shared across tasks, and its embeddings can be cached or precomputed when required by the deployment pipeline. These results indicate that integrating semantic context through multimodal fusion can significantly enhance intrusion detection in O-RAN while remaining competitive on standard IP-based IDS tasks. To the best of our knowledge, this work is among the first to study a complete multimodal LLM-based IDS pipeline for O-RAN data with cross-domain evaluation on both O-RAN and classical IP benchmarks. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Wirel. Commun. Netw. Conf. Workshops, WCNCW},
keywords = {Benchmarking, Computational linguistics, Computer crime, Cyber security, Cybersecurity, Embedded systems, Embeddings, Intelligent controllers, Internet protocols, Intrusion Detection, intrusion detection system, Intrusion Detection System (IDS), Intrusion Detection Systems, Language model, Large language model, Large Language Models (LLMs), Mobile telecommunication systems, Multi-modal, Multi-modal learning, Multimodal Learning, Network security, Open RAN, Pipelines, RAN intelligent controller, RAN intelligent controller (RIC), Semantics},
pubstate = {published},
tppubtype = {inproceedings}
}
Open Radio Access Network architectures introduce unprecedented openness and programmability through RAN Intelligent Controllers, expanding the attack surface beyond traditional volumetric threats. Most existing intrusion detection systems for fifth-generation mobile networks and ORAN rely primarily on numerical Key Performance Indicators (KPIs), often overlooking the security-relevant semantics embedded in logs and control messages. This paper proposes LLM4IDS, a semantic-aware and multimodal intrusion detection system that fuses lightweight Large Language Model (LLM) embeddings of textified events with traditional tabular KPIs in a compact Transformer-based classifier. Evaluated on the realworld OpenIreland O-RAN dataset and two classical IP network benchmarks (UNSW-NB15 and CICIDS2017), LLM4IDS consistently matches or surpasses strong tabular baselines while maintaining high efficiency. On OpenIreland, it reaches nearperfect detection (F1-score $textbackslashapprox 1.0$) and yields large gains for application-layer and volumetric attacks compared to KPI-only models. Across datasets, the classifier maintains an approximately 4 MB footprint and sub-millisecond CPU inference latency; the frozen sentence-LLM encoder can be shared across tasks, and its embeddings can be cached or precomputed when required by the deployment pipeline. These results indicate that integrating semantic context through multimodal fusion can significantly enhance intrusion detection in O-RAN while remaining competitive on standard IP-based IDS tasks. To the best of our knowledge, this work is among the first to study a complete multimodal LLM-based IDS pipeline for O-RAN data with cross-domain evaluation on both O-RAN and classical IP benchmarks. © 2026 IEEE.
3.
Brideau-Duquette, M.; Côté, S. S. -P.; Boukhalfi, T.; Renaud, P.
Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity Proceedings Article
In: B., Boulay; T., Di Mascio; E., Tovar; C., Meinel (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 856–864, Science and Technology Publications, Lda, 2025, ISBN: 21845026 (ISSN); 978-989758746-7 (ISBN), (Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services
@inproceedings{brideau-duquette_forensic_2025,
title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},
author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},
editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003621528&doi=10.5220%2f0013496100003932&partnerID=40&md5=ee442c9113f1104973b40ef698bc5d7d},
doi = {10.5220/0013496100003932},
isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},
volume = {1},
pages = {856–864},
publisher = {Science and Technology Publications, Lda},
abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},
note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},
keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
4.
Noor, A. F. Mohamed; Moghavvemi, S.; Tajudeen, F. P.; Motaghi, H.; Salehi, A.
Cybersecurity Readiness: A Review via Dynamic Capabilities Proceedings Article
In: M., Myers; R.A., Alias; W.F., Boh (Ed.): Pac. Asia Conf. Inf. Syst., Association for Information Systems, 2025, ISBN: 26896354 (ISSN).
Abstract | Links | BibTeX | Tags: Cybersecurity, dynamic capabilities, information security management, organizational cybersecurity, readiness
@inproceedings{mohamed_noor_cybersecurity_2025,
title = {Cybersecurity Readiness: A Review via Dynamic Capabilities},
author = {A. F. Mohamed Noor and S. Moghavvemi and F. P. Tajudeen and H. Motaghi and A. Salehi},
editor = {Myers M. and Alias R.A. and Boh W.F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029273577&partnerID=40&md5=2d20002e3605f51d6e87bd24f7af0cc0},
isbn = {26896354 (ISSN)},
year = {2025},
date = {2025-01-01},
booktitle = {Pac. Asia Conf. Inf. Syst.},
publisher = {Association for Information Systems},
abstract = {Understanding the factors influencing cybersecurity readiness is crucial for strategic investment in cyber capabilities. However, there is a gap in applying Dynamic Capabilities (DC) theory, which focuses on organizational agility in adapting to changing environments. This study fills this gap by using DC theory to analyze and categorize existing literature, offering a framework to classify cybersecurity readiness factors across different stages. This approach enhances our understanding of how organizations maintain a competitive edge amidst evolving cyber threats. The study addresses the following research question: What are the key factors influencing cybersecurity readiness in organizations, as discussed in literature from 2014 to 2023? Utilizing a thematic review with Atlas.ti 23, it examines literature from 2014 to 2023, providing a broad perspective on relevant factors. By applying DC theory, this research offers valuable insights for academics, organizations, and policymakers, aiding in strategy development and informed policymaking. © 2025, Association for Information Systems. All rights reserved.},
keywords = {Cybersecurity, dynamic capabilities, information security management, organizational cybersecurity, readiness},
pubstate = {published},
tppubtype = {inproceedings}
}
Understanding the factors influencing cybersecurity readiness is crucial for strategic investment in cyber capabilities. However, there is a gap in applying Dynamic Capabilities (DC) theory, which focuses on organizational agility in adapting to changing environments. This study fills this gap by using DC theory to analyze and categorize existing literature, offering a framework to classify cybersecurity readiness factors across different stages. This approach enhances our understanding of how organizations maintain a competitive edge amidst evolving cyber threats. The study addresses the following research question: What are the key factors influencing cybersecurity readiness in organizations, as discussed in literature from 2014 to 2023? Utilizing a thematic review with Atlas.ti 23, it examines literature from 2014 to 2023, providing a broad perspective on relevant factors. By applying DC theory, this research offers valuable insights for academics, organizations, and policymakers, aiding in strategy development and informed policymaking. © 2025, Association for Information Systems. All rights reserved.
5.
Brideau-Duquette, M.; Côté, S. S. -P.; Boukhalfi, T.; Renaud, P.
Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity Proceedings Article
In: B., Boulay; T., Di Mascio; E., Tovar; C., Meinel (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 856–864, Science and Technology Publications, Lda, 2025, ISBN: 21845026 (ISSN); 978-989758746-7 (ISBN), (Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services
@inproceedings{brideau-duquetteForensicPsychiatryBig2025a,
title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},
author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},
editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},
url = {https://www.scopus.com/pages/publications/105003621528?origin=resultslist},
doi = {10.5220/0013496100003932},
isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},
volume = {1},
pages = {856–864},
publisher = {Science and Technology Publications, Lda},
abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},
note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},
keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
6.
Noor, A. F. Mohamed; Moghavvemi, S.; Tajudeen, F. P.; Motaghi, H.; Salehi, A.
Cybersecurity Readiness: A Review via Dynamic Capabilities Proceedings Article
In: M., Myers; R.A., Alias; W.F., Boh (Ed.): Pac. Asia Conf. Inf. Syst., Association for Information Systems, 2025, ISBN: 26896354 (ISSN), (Journal Abbreviation: Pac. Asia Conf. Inf. Syst.).
Abstract | Links | BibTeX | Tags: Cybersecurity, dynamic capabilities, information security management, organizational cybersecurity, readiness
@inproceedings{mohamednoorCybersecurityReadinessReview2025,
title = {Cybersecurity Readiness: A Review via Dynamic Capabilities},
author = {A. F. Mohamed Noor and S. Moghavvemi and F. P. Tajudeen and H. Motaghi and A. Salehi},
editor = {Myers M. and Alias R.A. and Boh W.F.},
url = {https://www.scopus.com/pages/publications/105029273577?origin=resultslist},
isbn = {26896354 (ISSN)},
year = {2025},
date = {2025-01-01},
booktitle = {Pac. Asia Conf. Inf. Syst.},
publisher = {Association for Information Systems},
abstract = {Understanding the factors influencing cybersecurity readiness is crucial for strategic investment in cyber capabilities. However, there is a gap in applying Dynamic Capabilities (DC) theory, which focuses on organizational agility in adapting to changing environments. This study fills this gap by using DC theory to analyze and categorize existing literature, offering a framework to classify cybersecurity readiness factors across different stages. This approach enhances our understanding of how organizations maintain a competitive edge amidst evolving cyber threats. The study addresses the following research question: What are the key factors influencing cybersecurity readiness in organizations, as discussed in literature from 2014 to 2023? Utilizing a thematic review with Atlas.ti 23, it examines literature from 2014 to 2023, providing a broad perspective on relevant factors. By applying DC theory, this research offers valuable insights for academics, organizations, and policymakers, aiding in strategy development and informed policymaking. © 2025, Association for Information Systems. All rights reserved.},
note = {Journal Abbreviation: Pac. Asia Conf. Inf. Syst.},
keywords = {Cybersecurity, dynamic capabilities, information security management, organizational cybersecurity, readiness},
pubstate = {published},
tppubtype = {inproceedings}
}
Understanding the factors influencing cybersecurity readiness is crucial for strategic investment in cyber capabilities. However, there is a gap in applying Dynamic Capabilities (DC) theory, which focuses on organizational agility in adapting to changing environments. This study fills this gap by using DC theory to analyze and categorize existing literature, offering a framework to classify cybersecurity readiness factors across different stages. This approach enhances our understanding of how organizations maintain a competitive edge amidst evolving cyber threats. The study addresses the following research question: What are the key factors influencing cybersecurity readiness in organizations, as discussed in literature from 2014 to 2023? Utilizing a thematic review with Atlas.ti 23, it examines literature from 2014 to 2023, providing a broad perspective on relevant factors. By applying DC theory, this research offers valuable insights for academics, organizations, and policymakers, aiding in strategy development and informed policymaking. © 2025, Association for Information Systems. All rights reserved.
7.
Noor, A. F. Mohamed; Moghavvemi, S.; Tajudeen, F. P.; Motaghi, H.; Salehi, A.
Cybersecurity Readiness: A Review via Dynamic Capabilities Proceedings Article
In: M., Myers; R.A., Alias; W.F., Boh (Ed.): Pac. Asia Conf. Inf. Syst., Association for Information Systems, 2025, ISBN: 26896354 (ISSN).
Abstract | Links | BibTeX | Tags: Cybersecurity, dynamic capabilities, information security management, organizational cybersecurity, readiness
@inproceedings{mohamednoorCybersecurityReadinessReview2025a,
title = {Cybersecurity Readiness: A Review via Dynamic Capabilities},
author = {A. F. Mohamed Noor and S. Moghavvemi and F. P. Tajudeen and H. Motaghi and A. Salehi},
editor = {Myers M. and Alias R.A. and Boh W.F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029273577&partnerID=40&md5=2d20002e3605f51d6e87bd24f7af0cc0},
isbn = {26896354 (ISSN)},
year = {2025},
date = {2025-01-01},
booktitle = {Pac. Asia Conf. Inf. Syst.},
publisher = {Association for Information Systems},
abstract = {Understanding the factors influencing cybersecurity readiness is crucial for strategic investment in cyber capabilities. However, there is a gap in applying Dynamic Capabilities (DC) theory, which focuses on organizational agility in adapting to changing environments. This study fills this gap by using DC theory to analyze and categorize existing literature, offering a framework to classify cybersecurity readiness factors across different stages. This approach enhances our understanding of how organizations maintain a competitive edge amidst evolving cyber threats. The study addresses the following research question: What are the key factors influencing cybersecurity readiness in organizations, as discussed in literature from 2014 to 2023? Utilizing a thematic review with Atlas.ti 23, it examines literature from 2014 to 2023, providing a broad perspective on relevant factors. By applying DC theory, this research offers valuable insights for academics, organizations, and policymakers, aiding in strategy development and informed policymaking. © 2025, Association for Information Systems. All rights reserved.},
keywords = {Cybersecurity, dynamic capabilities, information security management, organizational cybersecurity, readiness},
pubstate = {published},
tppubtype = {inproceedings}
}
Understanding the factors influencing cybersecurity readiness is crucial for strategic investment in cyber capabilities. However, there is a gap in applying Dynamic Capabilities (DC) theory, which focuses on organizational agility in adapting to changing environments. This study fills this gap by using DC theory to analyze and categorize existing literature, offering a framework to classify cybersecurity readiness factors across different stages. This approach enhances our understanding of how organizations maintain a competitive edge amidst evolving cyber threats. The study addresses the following research question: What are the key factors influencing cybersecurity readiness in organizations, as discussed in literature from 2014 to 2023? Utilizing a thematic review with Atlas.ti 23, it examines literature from 2014 to 2023, providing a broad perspective on relevant factors. By applying DC theory, this research offers valuable insights for academics, organizations, and policymakers, aiding in strategy development and informed policymaking. © 2025, Association for Information Systems. All rights reserved.
8.
Brideau-Duquette, M.; Côté, S. S. -P.; Boukhalfi, T.; Renaud, P.
Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity Proceedings Article
In: B., Boulay; T., Di Mascio; E., Tovar; C., Meinel (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 856–864, Science and Technology Publications, Lda, 2025, ISBN: 21845026 (ISSN); 978-989758746-7 (ISBN), (Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services
@inproceedings{brideau-duquetteForensicPsychiatryBig2025,
title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},
author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},
editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003621528&doi=10.5220%2f0013496100003932&partnerID=40&md5=ee442c9113f1104973b40ef698bc5d7d},
doi = {10.5220/0013496100003932},
isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},
volume = {1},
pages = {856–864},
publisher = {Science and Technology Publications, Lda},
abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},
note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},
keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.