Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Zhu, Y.; Davoust, A.; Falk, T. H.
DeepSick: Deceiving Voice-Based Diagnostic Models with Synthetic Multilingual Pathological Speech Signals Article d'actes
Dans: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., p. 69–74, 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: COVID-19, Detection models, Diagnosis, Diagnostic model, Diagnostic systems, Generative model, Health assessments, Pathological conditions, Pathological speech signals, Scalable solution, Speech communication, Speech recognition, Speech synthesis, State of the art, Voice model
@inproceedings{zhuDeepSickDeceivingVoiceBased2025,
title = {DeepSick: Deceiving Voice-Based Diagnostic Models with Synthetic Multilingual Pathological Speech Signals},
author = {Y. Zhu and A. Davoust and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105033143787?origin=resultslist},
doi = {10.1109/SMC58881.2025.11343240},
isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
pages = {69–74},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Voice-based diagnostic systems offer a scalable solution for remote health assessment. However, recent advances in generative voice models may enable malicious manipulation of voice samples to simulate or conceal disease-related speech characteristics, which poses new risks to diagnostic systems. This paper investigates the vulnerability of diagnostic and detection models to such types of "deepfake"attacks. We show that it is possible to train a generative model to convert between healthy voices and pathological ones, which in turn, can successfully deceive existing diagnostic systems. Here, focus is placed on COVID-19 infection and respiratory abnormalities, but the method can be applied across different pathological conditions affecting vocal attributes. We also benchmark four state-of-the-art synthesized voice detection models on both real and generated pathological speech from three datasets. Our results show that current synthetic voice detectors, typically trained on healthy speech data, perform poorly on generated pathological samples. While fine-tuning with real pathological voices improves detection, a substantial performance gap remains. This work provides initial insights on an emerging threat to remote voice diagnostic systems that needs further work. © 2025 IEEE.},
note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
keywords = {COVID-19, Detection models, Diagnosis, Diagnostic model, Diagnostic systems, Generative model, Health assessments, Pathological conditions, Pathological speech signals, Scalable solution, Speech communication, Speech recognition, Speech synthesis, State of the art, Voice model},
pubstate = {published},
tppubtype = {inproceedings}
}
Voice-based diagnostic systems offer a scalable solution for remote health assessment. However, recent advances in generative voice models may enable malicious manipulation of voice samples to simulate or conceal disease-related speech characteristics, which poses new risks to diagnostic systems. This paper investigates the vulnerability of diagnostic and detection models to such types of "deepfake"attacks. We show that it is possible to train a generative model to convert between healthy voices and pathological ones, which in turn, can successfully deceive existing diagnostic systems. Here, focus is placed on COVID-19 infection and respiratory abnormalities, but the method can be applied across different pathological conditions affecting vocal attributes. We also benchmark four state-of-the-art synthesized voice detection models on both real and generated pathological speech from three datasets. Our results show that current synthetic voice detectors, typically trained on healthy speech data, perform poorly on generated pathological samples. While fine-tuning with real pathological voices improves detection, a substantial performance gap remains. This work provides initial insights on an emerging threat to remote voice diagnostic systems that needs further work. © 2025 IEEE.
2.
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.