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 Article d'actes
Dans: 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).
Résumé | Liens | BibTeX | Étiquettes: 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.
Laamari, A.; Moudoud, H.; Houda, Z. A. El
Lightweight LLM Adaptation for Intrusion Detection via Token-Efficient Flow Representation Article d'actes
Dans: IEEE Conf. Artif. Intell., CAI, p. 2122–2127, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833156039-3 (ISBN), (Journal Abbreviation: IEEE Conf. Artif. Intell., CAI).
Résumé | Liens | BibTeX | Étiquettes: Classification (of information), Data flow analysis, Decoder-only large language model, Decoder-only LLMs, decoding, Flow classification, Intrusion Detection, Intrusion-Detection, Language model, Large language model, LLMs, LoRA, Low-rank adaptation, Network Flow Classification, Network intrusion, Network security, Networks flows, Qwen2.5, Signal encoding, T5-Small, Token-oriented object notation, Tokenization, TOON
@inproceedings{laamariLightweightLLMAdaptation2026,
title = {Lightweight LLM Adaptation for Intrusion Detection via Token-Efficient Flow Representation},
author = {A. Laamari and H. Moudoud and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105042133342?origin=resultslist},
doi = {10.1109/CAI68641.2026.11536533},
isbn = {979-833156039-3 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Conf. Artif. Intell., CAI},
pages = {2122–2127},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Large language models (LLMs) are emerging as a promising approach for intrusion detection using structured network flow data. However, their practical deployment is constrained by context window limitations and the excessive token overhead introduced by conventional tabular serialization formats such as JSON. Verbose data representations inflate sequence lengths, often exceeding model input limits and causing feature truncation. Additionally, it remains unclear which LLM architecture is more suitable for structured intrusion detection tasks under limited training resources. To tackle this issue, we propose a novel representation-aware intrusion detection framework based on Token-Oriented Object Notation (TOON), a compact serialization format that maximizes token efficiency while preserving schema structure. Also, we integrate a Low-Rank Adaptation (LoRA) scheme to enable parameter-efficient fine-tuning. Finally, we evaluate the proposed framework as an encoder-decoder model (T5-Small) with a decoder-only model (Qwen2.5) on three benchmark datasets, including NSL-KDD, UNSW-NB15, and CIC-IDS2018 for binary and multi-class classification scenarios. The results show that our tokenizer-aligned, representation-aware preprocessing combined with lightweight encoder-decoder adaptation provides a practical and resource-efficient foundation for LLM-based intrusion detection. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},
keywords = {Classification (of information), Data flow analysis, Decoder-only large language model, Decoder-only LLMs, decoding, Flow classification, Intrusion Detection, Intrusion-Detection, Language model, Large language model, LLMs, LoRA, Low-rank adaptation, Network Flow Classification, Network intrusion, Network security, Networks flows, Qwen2.5, Signal encoding, T5-Small, Token-oriented object notation, Tokenization, TOON},
pubstate = {published},
tppubtype = {inproceedings}
}
Large language models (LLMs) are emerging as a promising approach for intrusion detection using structured network flow data. However, their practical deployment is constrained by context window limitations and the excessive token overhead introduced by conventional tabular serialization formats such as JSON. Verbose data representations inflate sequence lengths, often exceeding model input limits and causing feature truncation. Additionally, it remains unclear which LLM architecture is more suitable for structured intrusion detection tasks under limited training resources. To tackle this issue, we propose a novel representation-aware intrusion detection framework based on Token-Oriented Object Notation (TOON), a compact serialization format that maximizes token efficiency while preserving schema structure. Also, we integrate a Low-Rank Adaptation (LoRA) scheme to enable parameter-efficient fine-tuning. Finally, we evaluate the proposed framework as an encoder-decoder model (T5-Small) with a decoder-only model (Qwen2.5) on three benchmark datasets, including NSL-KDD, UNSW-NB15, and CIC-IDS2018 for binary and multi-class classification scenarios. The results show that our tokenizer-aligned, representation-aware preprocessing combined with lightweight encoder-decoder adaptation provides a practical and resource-efficient foundation for LLM-based intrusion detection. © 2026 IEEE.
3.
Selamnia, A.; Moudoud, H.; Khoukhi, L.; Brik, B.; Houda, Z. A. El
QSFL-ID: Quantum-Split Federated Learning for Intrusion Detection in IIoT Networks Article d'actes
Dans: IEEE Int Conf Commun, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 15503607 (ISSN); 979-831954209-0 (ISBN), (Journal Abbreviation: IEEE Int Conf Commun).
Résumé | Liens | BibTeX | Étiquettes: Automation, Complex networks, Cyber threats, Federated learning, IIoT, Industrial automation, Industrial internet of thing, Intrusion Detection, Intrusion-Detection, Learning systems, Machine learning methods, Network intrusion, Network security, Privacy-preserving techniques, Processing power, QML, Quantum circuit, Quantum entanglement, Split Learning, Variational quantum circuit, VQC
@inproceedings{selamniaQSFLIDQuantumSplitFederated2026,
title = {QSFL-ID: Quantum-Split Federated Learning for Intrusion Detection in IIoT Networks},
author = {A. Selamnia and H. Moudoud and L. Khoukhi and B. Brik and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105045419288?origin=resultslist},
doi = {10.1109/ICC59461.2026.11587037},
isbn = {15503607 (ISSN); 979-831954209-0 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Int Conf Commun},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The Industrial Internet of Things (IIoT) is reshaping industrial automation through interconnected, intelligent systems. However, this evolution increases exposure to sophisticated cyber threats, especially given the constraints of IIoT devices such as limited processing power, bandwidth, and heterogeneous protocols. Traditional machine learning methods often fail to meet these security demands due to their computational intensity and centralized data requirements. To address this, we propose a hybrid quantum-classical Split Federated Learning (SFL) framework for intrusion detection in IIoT networks. Our method integrates Variational Quantum Circuits (VQCs) to model complex, non-linear data relationships, enhancing detection accuracy while preserving data privacy through decentralized learning. The architecture assigns lightweight preprocessing to edge devices and complex analysis to a quantum backend, ensuring efficiency and scalability. To evaluate the proposed framework, we conduct extensive experiments on the real-world EDGE-IIoT dataset; the experimental results demonstrate that the model attains 95.5% training accuracy, significantly surpassing classical SFL (85.7%). In addition, the quantum model's enhanced entanglement properties and expressibility strengthen its generalization performance. This approach offers an efficient and privacy-preserving solution for securing IIoT systems. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Int Conf Commun},
keywords = {Automation, Complex networks, Cyber threats, Federated learning, IIoT, Industrial automation, Industrial internet of thing, Intrusion Detection, Intrusion-Detection, Learning systems, Machine learning methods, Network intrusion, Network security, Privacy-preserving techniques, Processing power, QML, Quantum circuit, Quantum entanglement, Split Learning, Variational quantum circuit, VQC},
pubstate = {published},
tppubtype = {inproceedings}
}
The Industrial Internet of Things (IIoT) is reshaping industrial automation through interconnected, intelligent systems. However, this evolution increases exposure to sophisticated cyber threats, especially given the constraints of IIoT devices such as limited processing power, bandwidth, and heterogeneous protocols. Traditional machine learning methods often fail to meet these security demands due to their computational intensity and centralized data requirements. To address this, we propose a hybrid quantum-classical Split Federated Learning (SFL) framework for intrusion detection in IIoT networks. Our method integrates Variational Quantum Circuits (VQCs) to model complex, non-linear data relationships, enhancing detection accuracy while preserving data privacy through decentralized learning. The architecture assigns lightweight preprocessing to edge devices and complex analysis to a quantum backend, ensuring efficiency and scalability. To evaluate the proposed framework, we conduct extensive experiments on the real-world EDGE-IIoT dataset; the experimental results demonstrate that the model attains 95.5% training accuracy, significantly surpassing classical SFL (85.7%). In addition, the quantum model's enhanced entanglement properties and expressibility strengthen its generalization performance. This approach offers an efficient and privacy-preserving solution for securing IIoT systems. © 2026 IEEE.
4.
Guerziz, I.; Falk, T.; Le, L. B.; Houda, Z. A. E.
Domain Adversarial Neural Networks with Adversarial Robustness Evaluation for Intrusion Detection Systems Article d'actes
Dans: K., Adi; O., Nguena Timo; N., Boulahia-Cuppens; D., Espes; N., Stakhanova; M., Omar (Ed.): Lect. Notes Comput. Sci., p. 153–165, Springer Science and Business Media Deutschland GmbH, 2026, ISBN: 03029743 (ISSN); 978-303220731-9 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Résumé | Liens | BibTeX | Étiquettes: Adversarial Attacks, Adversarial neural network, Adversarial Neural Networks, Attack Resilience, Attack resiliences, Computer crime, Domain adaptation, Fast gradient sign method, FGSM, Gradient-descent, Intrusion Detection, Intrusion Detection Systems, Network intrusion, Network intrusion detection systems, Network security, Neural networks, Neural-networks, PGD, Projected gradient, Projected gradient descent
@inproceedings{guerzizDomainAdversarialNeural2026,
title = {Domain Adversarial Neural Networks with Adversarial Robustness Evaluation for Intrusion Detection Systems},
author = {I. Guerziz and T. Falk and L. B. Le and Z. A. E. Houda},
editor = {Adi K. and Nguena Timo O. and Boulahia-Cuppens N. and Espes D. and Stakhanova N. and Omar M.},
url = {https://www.scopus.com/pages/publications/105046102448?origin=resultslist},
doi = {10.1007/978-3-032-20732-6_10},
isbn = {03029743 (ISSN); 978-303220731-9 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {16295 LNCS},
pages = {153–165},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Modern Network Intrusion Detection Systems (NIDS) face the dual challenge of maintaining performance across diverse network environments while resisting adversarial manipulations. This paper investigates the intersection of domain adaptation and adversarial robustness in NIDS, a topic that has not been extensively studied. We implement a Domain-Adversarial Neural Network (DANN) with dynamic gradient reversal to adapt models from NSL-KDD to UNSW-NB15. To evaluate security, we assess the model under Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks. Additionally, we introduce FGSM-based adversarial training to enhance robustness. Our results show that while domain adaptation improves cross-domain detection, it also increases susceptibility to adversarial attacks. Incorporating adversarial training mitigates this vulnerability, improving resilience without compromising performance on clean data. These findings provide key insights for designing adaptive and secure intrusion detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial Attacks, Adversarial neural network, Adversarial Neural Networks, Attack Resilience, Attack resiliences, Computer crime, Domain adaptation, Fast gradient sign method, FGSM, Gradient-descent, Intrusion Detection, Intrusion Detection Systems, Network intrusion, Network intrusion detection systems, Network security, Neural networks, Neural-networks, PGD, Projected gradient, Projected gradient descent},
pubstate = {published},
tppubtype = {inproceedings}
}
Modern Network Intrusion Detection Systems (NIDS) face the dual challenge of maintaining performance across diverse network environments while resisting adversarial manipulations. This paper investigates the intersection of domain adaptation and adversarial robustness in NIDS, a topic that has not been extensively studied. We implement a Domain-Adversarial Neural Network (DANN) with dynamic gradient reversal to adapt models from NSL-KDD to UNSW-NB15. To evaluate security, we assess the model under Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks. Additionally, we introduce FGSM-based adversarial training to enhance robustness. Our results show that while domain adaptation improves cross-domain detection, it also increases susceptibility to adversarial attacks. Incorporating adversarial training mitigates this vulnerability, improving resilience without compromising performance on clean data. These findings provide key insights for designing adaptive and secure intrusion detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
5.
Zoungrana, A. F.; Moudoud, H.; Tajeuna, E. G.; Adi, K.
Adversarial Ensemble Framework: Leveraging GANs for Robust Intrusion Detection in IoT Networks Article d'actes
Dans: K., Adi; O., Nguena Timo; N., Boulahia-Cuppens; D., Espes; N., Stakhanova; M., Omar (Ed.): Lect. Notes Comput. Sci., p. 85–99, Springer Science and Business Media Deutschland GmbH, 2026, ISBN: 03029743 (ISSN); 978-303220731-9 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Résumé | Liens | BibTeX | Étiquettes: Adversarial networks, Class imbalance, Computer crime, Concept drifts, Gallium nitride, Generative adversarial networks, Internet of thing network, Internet of things, Intrusion Detection, Intrusion Detection Systems, Intrusion-Detection, IoT Networks, Key Issues, Network intrusion, Network security, Rapid expansion, Security, Security challenges
@inproceedings{zoungranaAdversarialEnsembleFramework2026,
title = {Adversarial Ensemble Framework: Leveraging GANs for Robust Intrusion Detection in IoT Networks},
author = {A. F. Zoungrana and H. Moudoud and E. G. Tajeuna and K. Adi},
editor = {Adi K. and Nguena Timo O. and Boulahia-Cuppens N. and Espes D. and Stakhanova N. and Omar M.},
url = {https://www.scopus.com/pages/publications/105046136116?origin=resultslist},
doi = {10.1007/978-3-032-20732-6_6},
isbn = {03029743 (ISSN); 978-303220731-9 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {16295 LNCS},
pages = {85–99},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The rapid expansion of Internet of Things (IoT) devices introduces complex security challenges that traditional intrusion detection systems struggle to address. This paper proposes an Adversarial Ensemble Framework using Generative Adversarial Networks (GANs) to improve the accuracy and resilience of intrusion detection in IoT environments. The framework tackles key issues such as class imbalance, concept drift, and adversarial attacks by employing multiple GAN variants such as Vanilla GAN, Conditional GAN (CGAN), and Wasserstein GAN (WGAN) to generate high-quality synthetic attack data. A dynamic ensemble learning mechanism selects the most effective model for each attack type based on performance metrics. Experiments on NSL-KDD and CIC-IDS2017 show that WGAN yields the most effective synthetic data, contributing to a detection rate of up to 96%. The approach proves particularly effective in identifying rare attacks, making it a scalable and adaptive solution for IoT security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial networks, Class imbalance, Computer crime, Concept drifts, Gallium nitride, Generative adversarial networks, Internet of thing network, Internet of things, Intrusion Detection, Intrusion Detection Systems, Intrusion-Detection, IoT Networks, Key Issues, Network intrusion, Network security, Rapid expansion, Security, Security challenges},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapid expansion of Internet of Things (IoT) devices introduces complex security challenges that traditional intrusion detection systems struggle to address. This paper proposes an Adversarial Ensemble Framework using Generative Adversarial Networks (GANs) to improve the accuracy and resilience of intrusion detection in IoT environments. The framework tackles key issues such as class imbalance, concept drift, and adversarial attacks by employing multiple GAN variants such as Vanilla GAN, Conditional GAN (CGAN), and Wasserstein GAN (WGAN) to generate high-quality synthetic attack data. A dynamic ensemble learning mechanism selects the most effective model for each attack type based on performance metrics. Experiments on NSL-KDD and CIC-IDS2017 show that WGAN yields the most effective synthetic data, contributing to a detection rate of up to 96%. The approach proves particularly effective in identifying rare attacks, making it a scalable and adaptive solution for IoT security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
6.
Moudoud, H.; Houda, Z. A. El; Khoukhi, L.; Mouftah, H. T.
An SDN-based Adaptive Ensemble Learning Framework for Intrusion Mitigation in Wireless Networks Article d'actes
Dans: M., Valenti; D., Reed; M., Torres (Ed.): IEEE Int Conf Commun, p. 554–559, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 15503607 (ISSN); 979-833150521-9 (ISBN), (Journal Abbreviation: IEEE Int Conf Commun).
Résumé | Liens | BibTeX | Étiquettes: Aerial vehicle, Antennas, Artificial intelligence, Computer crime, Ensemble learning, Intrusion Detection, Intrusion Detection Systems, Jamming, Jamming Attacks, Learning algorithms, Learning frameworks, Network intrusion, Network operations, Radio communication, Security systems, Security threats, Sensors network, Unmanned aerial vehicle, Unmanned Aerial Vehicles, Unmanned aerial vehicles (UAV), Wireless networks, Wireless sensor, Wireless Sensor Networks, Zero-day attack
@inproceedings{moudoudSDNbasedAdaptiveEnsemble2025,
title = {An SDN-based Adaptive Ensemble Learning Framework for Intrusion Mitigation in Wireless Networks},
author = {H. Moudoud and Z. A. El Houda and L. Khoukhi and H. T. Mouftah},
editor = {Valenti M. and Reed D. and Torres M.},
url = {https://www.scopus.com/pages/publications/105018460686?origin=resultslist},
doi = {10.1109/ICC52391.2025.11161745},
isbn = {15503607 (ISSN); 979-833150521-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IEEE Int Conf Commun},
pages = {554–559},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Jamming attacks are among the most critical security threats to Wireless Sensor Networks (WSNs), as they can severely disrupt normal network operations, leading to data loss, network downtime, and reduced system performance. Intrusion Detection Systems (IDSs) have therefore become essential to protect WSNs. However, conventional IDSs often struggle to detect zero-day attacks, creating a significant security gap. To address this, Artificial Intelligence (AI)-based IDSs have been introduced, offering improved detection capabilities but frequently encountering high bias or variance issues, which reduce their reliability. Recently, ensemble learning (EL) has emerged as a promising approach to build more adaptable and data-resilient models by combining multiple learning algorithms. In this context, we propose AdaptiveBoost, an SDN-based Adaptive Ensemble Learning Framework, specifically designed for effective jamming attack detection in WSNs. The SDN integration allows AdaptiveBoost to optimize network traffic flow, identify anomalies in real-time, and adaptively fine-tune detection mechanisms based on current network conditions. We conduct several experiments to evaluate AdaptiveBoost using real-world WSN attacks; using the well-known public network security dataset, WSN-DS, show that AdaptiveBoost outperforms AI-based algorithms in terms of accuracy, precision, recall, and F1 score, while achieving a remarkable reduction in training time by a factor of 235, making it an efficient, scalable solution for securing WSNs against jamming attacks. © 2025 IEEE.},
note = {Journal Abbreviation: IEEE Int Conf Commun},
keywords = {Aerial vehicle, Antennas, Artificial intelligence, Computer crime, Ensemble learning, Intrusion Detection, Intrusion Detection Systems, Jamming, Jamming Attacks, Learning algorithms, Learning frameworks, Network intrusion, Network operations, Radio communication, Security systems, Security threats, Sensors network, Unmanned aerial vehicle, Unmanned Aerial Vehicles, Unmanned aerial vehicles (UAV), Wireless networks, Wireless sensor, Wireless Sensor Networks, Zero-day attack},
pubstate = {published},
tppubtype = {inproceedings}
}
Jamming attacks are among the most critical security threats to Wireless Sensor Networks (WSNs), as they can severely disrupt normal network operations, leading to data loss, network downtime, and reduced system performance. Intrusion Detection Systems (IDSs) have therefore become essential to protect WSNs. However, conventional IDSs often struggle to detect zero-day attacks, creating a significant security gap. To address this, Artificial Intelligence (AI)-based IDSs have been introduced, offering improved detection capabilities but frequently encountering high bias or variance issues, which reduce their reliability. Recently, ensemble learning (EL) has emerged as a promising approach to build more adaptable and data-resilient models by combining multiple learning algorithms. In this context, we propose AdaptiveBoost, an SDN-based Adaptive Ensemble Learning Framework, specifically designed for effective jamming attack detection in WSNs. The SDN integration allows AdaptiveBoost to optimize network traffic flow, identify anomalies in real-time, and adaptively fine-tune detection mechanisms based on current network conditions. We conduct several experiments to evaluate AdaptiveBoost using real-world WSN attacks; using the well-known public network security dataset, WSN-DS, show that AdaptiveBoost outperforms AI-based algorithms in terms of accuracy, precision, recall, and F1 score, while achieving a remarkable reduction in training time by a factor of 235, making it an efficient, scalable solution for securing WSNs against jamming attacks. © 2025 IEEE.
7.
Kadi, A.; Selamnia, A.; Houda, Z. A. E.; Moudoud, H.; Brik, B.; Khoukhi, L.
An In-Depth Comparative Study of Quantum-Classical Encoding Methods for Network Intrusion Detection Article de journal
Dans: IEEE Open Journal of the Communications Society, vol. 6, p. 1129–1148, 2025, ISSN: 2644125X (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Adversarial machine learning, Cyber attacks, Embeddings, Encoding methods, Encoding techniques, Encodings, Intrusion Detection, intrusion detection system, Intrusion Detection Systems, Machine-learning, Network embeddings, Network intrusion, Quantum cryptography, Quantum efficiency, Quantum electronics, Quantum machine learning, Quantum machines, Quantum-classical, Quantum-classical encoding, Zero-day attack
@article{kadiInDepthComparativeStudy2025,
title = {An In-Depth Comparative Study of Quantum-Classical Encoding Methods for Network Intrusion Detection},
author = {A. Kadi and A. Selamnia and Z. A. E. Houda and H. Moudoud and B. Brik and L. Khoukhi},
url = {https://www.scopus.com/pages/publications/85217024576?origin=resultslist},
doi = {10.1109/OJCOMS.2025.3537957},
issn = {2644125X (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Open Journal of the Communications Society},
volume = {6},
pages = {1129–1148},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In today's rapidly evolving cyber landscape, the growing sophistication of attacks, including the rise of zero-day exploits, poses critical challenges for network intrusion detection. Traditional Intrusion Detection Systems (IDSs) often struggle with the complexity and high dimensionality of modern cyber threats. Quantum Machine Learning (QML) seamlessly integrates the computational power of quantum computing with the adaptability of machine learning, offering an innovative approach to solving intricate and high-dimensional challenges. A key factor in QML's performance is the method used to encode classical data into quantum states, as it defines how data is represented and processed in quantum circuits. QML offers promising advances for IDS, particularly through hybrid quantum-classical models. This study presents an in-depth comparative analysis of quantum-classical data encoding techniques for QML-based IDS. To the best of our knowledge, this is the first study to comprehensively evaluate the performance impact of different quantum encoding methods and provide a thorough evaluation of their impacts on the overall model performances. To achieve this, we first present a comprehensive evaluation of quantum and classical data encoding techniques, focusing on four key encoding techniques namely, Amplitude Embedding, Angle Embedding, Instantaneous Quantum Polynomial (IQP) Encoding, and Quantum Approximate Optimization Algorithm (QAOA) Embedding. Then, we develop a hybrid quantum-classical QML model to analyze how each encoding affects classification performance for malicious traffic. Finally, we conduct extensive experiments using two well-known, real-world network attack datasets to assess the accuracy and efficiency of each encoding approach. Our obtained results show notable differences in classification accuracy, underscoring the importance of encoding choice in optimizing QML-based IDS. This study aims to advance the application of quantum methodologies in network security by identifying effective encoding strategies for intrusion detection. © 2025 IEEE.},
keywords = {Adversarial machine learning, Cyber attacks, Embeddings, Encoding methods, Encoding techniques, Encodings, Intrusion Detection, intrusion detection system, Intrusion Detection Systems, Machine-learning, Network embeddings, Network intrusion, Quantum cryptography, Quantum efficiency, Quantum electronics, Quantum machine learning, Quantum machines, Quantum-classical, Quantum-classical encoding, Zero-day attack},
pubstate = {published},
tppubtype = {article}
}
In today's rapidly evolving cyber landscape, the growing sophistication of attacks, including the rise of zero-day exploits, poses critical challenges for network intrusion detection. Traditional Intrusion Detection Systems (IDSs) often struggle with the complexity and high dimensionality of modern cyber threats. Quantum Machine Learning (QML) seamlessly integrates the computational power of quantum computing with the adaptability of machine learning, offering an innovative approach to solving intricate and high-dimensional challenges. A key factor in QML's performance is the method used to encode classical data into quantum states, as it defines how data is represented and processed in quantum circuits. QML offers promising advances for IDS, particularly through hybrid quantum-classical models. This study presents an in-depth comparative analysis of quantum-classical data encoding techniques for QML-based IDS. To the best of our knowledge, this is the first study to comprehensively evaluate the performance impact of different quantum encoding methods and provide a thorough evaluation of their impacts on the overall model performances. To achieve this, we first present a comprehensive evaluation of quantum and classical data encoding techniques, focusing on four key encoding techniques namely, Amplitude Embedding, Angle Embedding, Instantaneous Quantum Polynomial (IQP) Encoding, and Quantum Approximate Optimization Algorithm (QAOA) Embedding. Then, we develop a hybrid quantum-classical QML model to analyze how each encoding affects classification performance for malicious traffic. Finally, we conduct extensive experiments using two well-known, real-world network attack datasets to assess the accuracy and efficiency of each encoding approach. Our obtained results show notable differences in classification accuracy, underscoring the importance of encoding choice in optimizing QML-based IDS. This study aims to advance the application of quantum methodologies in network security by identifying effective encoding strategies for intrusion detection. © 2025 IEEE.
8.
Soltani, N.; Nejadshamsi, S.; Houda, Z. A. El; Khoury, R.; Costa, K. A. P.; Falk, T. H.; Avila, A. R.
Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks Article d'actes
Dans: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., p. 39–44, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 1062922X (ISSN); 979-833153358-8 (ISBN), (Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.).
Résumé | Liens | BibTeX | Étiquettes: Adversarial machine learning, Adversarial networks, Classification (of information), Computer crime, Fast gradient sign method, Fast Gradient Sign Method (FGSM), Generative adversarial network, Generative Adversarial Network (GAN), Generative adversarial networks, Generative model, Generative Models, Intrusion Detection, Intrusion-Detection, Learning algorithms, Learning systems, Machine-learning, Multi-layers, Network intrusion, Network intrusion detection systems, Network layers, Network security, Second layer, Stackings
@inproceedings{soltaniEnhancingNetworkIntrusion2025,
title = {Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks},
author = {N. Soltani and S. Nejadshamsi and Z. A. El Houda and R. Khoury and K. A. P. Costa and T. H. Falk and A. R. Avila},
url = {https://www.scopus.com/pages/publications/105033159769?origin=resultslist},
doi = {10.1109/SMC58881.2025.11342479},
isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
pages = {39–44},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Adversarial examples can represent a serious threat to machine learning (ML) algorithms. If used to manipulate the behaviour of ML-based Network Intrusion Detection Systems (NIDS), they can jeopardize network security. In this work, we aim to mitigate such risks by increasing the robustness of NIDS towards adversarial attacks. To that end, we explore two adversarial methods for generating malicious network traffic. The first method is based on Generative Adversarial Networks (GAN) and the second one is the Fast Gradient Sign Method (FGSM). The adversarial examples generated by these methods are then used to evaluate a novel multilayer defense mechanism, specifically designed to mitigate the vulnerability of ML-based NIDS. Our solution consists of one layer of stacking classifiers and a second layer based on an autoencoder. If the incoming network data are classified as benign by the first layer, the second layer is activated to ensure that the decision made by the stacking classifier is correct. We also incorporated adversarial training to further improve the robustness of our solution. Experiments on two datasets, namely UNSW-NB15 and NSL-KDD, demonstrate that the proposed approach increases resilience to adversarial attacks. © 2025 IEEE.},
note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
keywords = {Adversarial machine learning, Adversarial networks, Classification (of information), Computer crime, Fast gradient sign method, Fast Gradient Sign Method (FGSM), Generative adversarial network, Generative Adversarial Network (GAN), Generative adversarial networks, Generative model, Generative Models, Intrusion Detection, Intrusion-Detection, Learning algorithms, Learning systems, Machine-learning, Multi-layers, Network intrusion, Network intrusion detection systems, Network layers, Network security, Second layer, Stackings},
pubstate = {published},
tppubtype = {inproceedings}
}
Adversarial examples can represent a serious threat to machine learning (ML) algorithms. If used to manipulate the behaviour of ML-based Network Intrusion Detection Systems (NIDS), they can jeopardize network security. In this work, we aim to mitigate such risks by increasing the robustness of NIDS towards adversarial attacks. To that end, we explore two adversarial methods for generating malicious network traffic. The first method is based on Generative Adversarial Networks (GAN) and the second one is the Fast Gradient Sign Method (FGSM). The adversarial examples generated by these methods are then used to evaluate a novel multilayer defense mechanism, specifically designed to mitigate the vulnerability of ML-based NIDS. Our solution consists of one layer of stacking classifiers and a second layer based on an autoencoder. If the incoming network data are classified as benign by the first layer, the second layer is activated to ensure that the decision made by the stacking classifier is correct. We also incorporated adversarial training to further improve the robustness of our solution. Experiments on two datasets, namely UNSW-NB15 and NSL-KDD, demonstrate that the proposed approach increases resilience to adversarial attacks. © 2025 IEEE.