Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Malasi, J. -J. M.; Moudoud, H.; Missaoui, R.
CausalGraph: When Causal Reasoning Meets Large Language Models for Intrusion Detection Systems 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: Alarm systems, Budget control, causal reasoning, Chains, Computer crime, Concept Drift, Concept drifts, Conformal Risk Control, Counterfactuals, Generative AI, Human computer interaction, Intrusion Detection, Intrusion Detection Systems, Intrusion-Detection, Knowledge based systems, Language model, LLM, LLMs, Network intrusion, Network security, Risks controls, Sampling
@inproceedings{malasiCausalGraphWhenCausal2026,
title = {CausalGraph: When Causal Reasoning Meets Large Language Models for Intrusion Detection Systems},
author = {J. -J. M. Malasi and H. Moudoud and R. Missaoui},
url = {https://www.scopus.com/pages/publications/105037367854?origin=resultslist},
doi = {10.1109/ICCE67443.2026.11449614},
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 intrusion detection systems often achieve impressive benchmark accuracy yet fail in real-world deployment, where network behavior and attacker tactics continuously evolve. Under concept drift, decision boundaries learned offline can diverge from operational reality, triggering false-alarm cascades and creating detection blind spots that erode analyst trust. In this paper, we propose CausalGraph-IDS, a causal and language-model-assisted intrusion detection framework that moves beyond purely correlational scoring by explicitly verifying multi-stage attack chains. Additionally, we propose CHAIN-CRC, a unified algorithm that (i) learns a constrained attack-chain causal graph guided by knowledge-based priors derived from widely used adversary behavior taxonomies, (ii) computes robustness scores by testing whether alarms persist under feasible counterfactual security interventions, and (iii) applies conformal risk control to enforce operator-defined false-positive budgets with finite-sample guarantees.Generative models are integrated in strictly assistive roles through three modules: prior induction to distill causal constraints from unstructured threat reports, counterfactual generation to propose realistic and operationally feasible interventions, and causal logic justification to produce human-readable explanations grounded in the learned attack chain. Experiments on two widely used network intrusion detection benchmarks show that CausalGraph-IDS provides robust, explainable, and risk-governed detection, maintaining strong recall at low false-positive rates while delivering actionable causal insights for mitigation. © 2026 IEEE.},
note = {Journal Abbreviation: Dig Tech Pap IEEE Int Conf Consum Electron},
keywords = {Alarm systems, Budget control, causal reasoning, Chains, Computer crime, Concept Drift, Concept drifts, Conformal Risk Control, Counterfactuals, Generative AI, Human computer interaction, Intrusion Detection, Intrusion Detection Systems, Intrusion-Detection, Knowledge based systems, Language model, LLM, LLMs, Network intrusion, Network security, Risks controls, Sampling},
pubstate = {published},
tppubtype = {inproceedings}
}
Modern intrusion detection systems often achieve impressive benchmark accuracy yet fail in real-world deployment, where network behavior and attacker tactics continuously evolve. Under concept drift, decision boundaries learned offline can diverge from operational reality, triggering false-alarm cascades and creating detection blind spots that erode analyst trust. In this paper, we propose CausalGraph-IDS, a causal and language-model-assisted intrusion detection framework that moves beyond purely correlational scoring by explicitly verifying multi-stage attack chains. Additionally, we propose CHAIN-CRC, a unified algorithm that (i) learns a constrained attack-chain causal graph guided by knowledge-based priors derived from widely used adversary behavior taxonomies, (ii) computes robustness scores by testing whether alarms persist under feasible counterfactual security interventions, and (iii) applies conformal risk control to enforce operator-defined false-positive budgets with finite-sample guarantees.Generative models are integrated in strictly assistive roles through three modules: prior induction to distill causal constraints from unstructured threat reports, counterfactual generation to propose realistic and operationally feasible interventions, and causal logic justification to produce human-readable explanations grounded in the learned attack chain. Experiments on two widely used network intrusion detection benchmarks show that CausalGraph-IDS provides robust, explainable, and risk-governed detection, maintaining strong recall at low false-positive rates while delivering actionable causal insights for mitigation. © 2026 IEEE.
2.
Khosrojerdi, F.; Gagnon, S.; Valverde, R.
Identifying Influential Factors Affecting the Shading of a Solar Panel Proceedings Article
In: 2021 IEEE Electrical Power and Energy Conference, EPEC 2021, pp. 255–260, Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 978-1-66542-928-3.
Abstract | Links | BibTeX | Tags: Condition, Energy forecasting, Forecasting, Influential factors, Knowledge based systems, Partial shading, Photovoltaic planning, Photovoltaic power plant, Photovoltaics, Solar energy, Solar energy forecasting, Solar panels, Uniformly shading
@inproceedings{khosrojerdi_identifying_2021,
title = {Identifying Influential Factors Affecting the Shading of a Solar Panel},
author = {F. Khosrojerdi and S. Gagnon and R. Valverde},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123618954&doi=10.1109%2fEPEC52095.2021.9621688&partnerID=40&md5=089defe4cb4ff62c6d3a367d1c6260d1},
doi = {10.1109/EPEC52095.2021.9621688},
isbn = {978-1-66542-928-3},
year = {2021},
date = {2021-01-01},
booktitle = {2021 IEEE Electrical Power and Energy Conference, EPEC 2021},
pages = {255–260},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Photovoltaic (PV) systems produce less energy when operating under shadings. PV planners need to identify important factors affecting the shadings to forecast power generations in various ambient conditions. Using a case study, we show that overlooking the impact of an environmental factor, herein snowfalls, will result in overestimations in the power forecasting. In this paper, we study the context of the shading from different perspectives and introduce parameters that can affect the duration and severity of shading conditions. To identify key notions of the shading and important factors involved, we implement a literature review and include experts' knowledge by exploring PV planning tools and conducting a survey in the sector of solar energy. The identified factors can be used to develop a knowledge-based model representing key concepts associated with shading conditions. In addition, the identification of important factors affecting the duration and severity of shading conditions addresses new research domains that need to be explored in the field of PV shading and power estimation. © 2021 IEEE.},
keywords = {Condition, Energy forecasting, Forecasting, Influential factors, Knowledge based systems, Partial shading, Photovoltaic planning, Photovoltaic power plant, Photovoltaics, Solar energy, Solar energy forecasting, Solar panels, Uniformly shading},
pubstate = {published},
tppubtype = {inproceedings}
}
Photovoltaic (PV) systems produce less energy when operating under shadings. PV planners need to identify important factors affecting the shadings to forecast power generations in various ambient conditions. Using a case study, we show that overlooking the impact of an environmental factor, herein snowfalls, will result in overestimations in the power forecasting. In this paper, we study the context of the shading from different perspectives and introduce parameters that can affect the duration and severity of shading conditions. To identify key notions of the shading and important factors involved, we implement a literature review and include experts' knowledge by exploring PV planning tools and conducting a survey in the sector of solar energy. The identified factors can be used to develop a knowledge-based model representing key concepts associated with shading conditions. In addition, the identification of important factors affecting the duration and severity of shading conditions addresses new research domains that need to be explored in the field of PV shading and power estimation. © 2021 IEEE.