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.
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.
2.
Allaoui, M.; Hedjam, R.; Bouanane, K.; Allili, M. S.; Kherfi, M. L.; Belhaouari, S. B.
Exploring non-negativity for improved manifold embedding: Application to t-SNE Journal Article
In: Knowledge-Based Systems, vol. 330, 2025, ISSN: 09507051 (ISSN).
Abstract | Links | BibTeX | Tags: Dimensionality reduction, Embedding technique, Embeddings, Gradient methods, Gradient-descent, Manifold embedding, Matrix algebra, Non-negative matrix factorization, Non-negativity, Nonnegative matrix factorization, Nonnegativity constraints, Performance, T-SNE
@article{allaoui_exploring_2025,
title = {Exploring non-negativity for improved manifold embedding: Application to t-SNE},
author = {M. Allaoui and R. Hedjam and K. Bouanane and M. S. Allili and M. L. Kherfi and S. B. Belhaouari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018098090&doi=10.1016%2Fj.knosys.2025.114547&partnerID=40&md5=237540c38a928146d589b96cd6888547},
doi = {10.1016/j.knosys.2025.114547},
issn = {09507051 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Knowledge-Based Systems},
volume = {330},
abstract = {Drawing inspiration from Non-negative Matrix Factorization (NMF), this paper explores the potential of incorporating non-negativity constraints into embedding techniques, with a focus on t-SNE as an application. Specifically, we investigate the following questions: Can enforcing non-negativity in the embedding space enhance interpretability and improve the quality of embedded data? By prioritizing non-negativity, can embedding methods achieve better performance and more meaningful representations? Additionally, does enforcing non-negativity in the embedded space help preserve both the local and global structure of data in the manifold, leading to more accurate and interpretable embeddings? In this work, we could show both objectively and subjectively how enforcing t-SNE to leverage the non-negativity of the data addresses the raised questions. To achieve this, we introduced a novel approach to transforming the additive update rule of the gradient descent used by t-SNE to a multiplicative counterpart to enforce the non-negativity in the embedded space. However, grappling with full non-negativity in the gradient descent formula presents challenges, prompting our focus solely on the (yi−yj) term, resulting in a semi-non-negative t-SNE algorithm, shortly named SN-tSNE. Nevertheless, experimental findings substantiate the significant impact of the proposed update rule on the performance and efficacy of the SN-tSNE algorithm. Furthermore, additional experiments are performed to compare SN-tSNE with its precursor t-SNE, as well as the competitive embedding technique UMAP, alongside other relevant embedding and dimensionality reduction models like NMF. The source code of SN-tSNE is available on GitHub (https://github.com/M-Allaoui/SN-tSNE.git). © 2025},
keywords = {Dimensionality reduction, Embedding technique, Embeddings, Gradient methods, Gradient-descent, Manifold embedding, Matrix algebra, Non-negative matrix factorization, Non-negativity, Nonnegative matrix factorization, Nonnegativity constraints, Performance, T-SNE},
pubstate = {published},
tppubtype = {article}
}
Drawing inspiration from Non-negative Matrix Factorization (NMF), this paper explores the potential of incorporating non-negativity constraints into embedding techniques, with a focus on t-SNE as an application. Specifically, we investigate the following questions: Can enforcing non-negativity in the embedding space enhance interpretability and improve the quality of embedded data? By prioritizing non-negativity, can embedding methods achieve better performance and more meaningful representations? Additionally, does enforcing non-negativity in the embedded space help preserve both the local and global structure of data in the manifold, leading to more accurate and interpretable embeddings? In this work, we could show both objectively and subjectively how enforcing t-SNE to leverage the non-negativity of the data addresses the raised questions. To achieve this, we introduced a novel approach to transforming the additive update rule of the gradient descent used by t-SNE to a multiplicative counterpart to enforce the non-negativity in the embedded space. However, grappling with full non-negativity in the gradient descent formula presents challenges, prompting our focus solely on the (yi−yj) term, resulting in a semi-non-negative t-SNE algorithm, shortly named SN-tSNE. Nevertheless, experimental findings substantiate the significant impact of the proposed update rule on the performance and efficacy of the SN-tSNE algorithm. Furthermore, additional experiments are performed to compare SN-tSNE with its precursor t-SNE, as well as the competitive embedding technique UMAP, alongside other relevant embedding and dimensionality reduction models like NMF. The source code of SN-tSNE is available on GitHub (https://github.com/M-Allaoui/SN-tSNE.git). © 2025
3.
Souza, J. V.; Amamou, H.; Chen, R.; Salari, E.; Gubelmann, R.; Niklaus, C.; Serpa, T.; Lima, M. M. F.; Pinto, P. T.; Kshirsagar, S.; Davoust, A.; Handschuh, S.; Avila, A. R.
Cross-Lingual Keyword Extraction for Pesticide Terminology in Brazilian Portuguese and English Journal Article
In: Journal of the Brazilian Computer Society, vol. 31, no. 1, pp. 973–990, 2025, ISSN: 01046500 (ISSN).
Abstract | Links | BibTeX | Tags: Agriculture, BERT embedding, BERT embeddings, Cross-lingual, Embeddings, extraction, Food consumption, Keywords extraction, Labelings, Low resource languages, Multilingual extraction, Pesticides, Technical terms, Terminology, Word alignment
@article{de_souza_cross-lingual_2025,
title = {Cross-Lingual Keyword Extraction for Pesticide Terminology in Brazilian Portuguese and English},
author = {J. V. Souza and H. Amamou and R. Chen and E. Salari and R. Gubelmann and C. Niklaus and T. Serpa and M. M. F. Lima and P. T. Pinto and S. Kshirsagar and A. Davoust and S. Handschuh and A. R. Avila},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105019700300&doi=10.5753%2Fjbcs.2025.5815&partnerID=40&md5=85ee75baf4550666a307310cd04d1c83},
doi = {10.5753/jbcs.2025.5815},
issn = {01046500 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Journal of the Brazilian Computer Society},
volume = {31},
number = {1},
pages = {973–990},
abstract = {Agriculture plays a crucial role in Brazil’s economy. As the country intensifies its activities in the sector, the use of pesticides also increases. Hence, the risks associated with pesticide-laden food consumption have become a concern for chemistry researchers. An issue affecting regulatory standardization of pesticides in Brazil is the difficulty in translating pesticide names, particularly from English. For example, the word malathion can be translated from English to Portuguese as malatiom or malatião, resulting in inconsistent labeling. This issue extends to the broader problem of translating highly technical terms between languages, in particular for low-resource languages. In this work, we investigate terminological variation in the chemistry of organophosphorus pesticides. Our goal is to study strategies for domain-specific multilingual keyword extraction. To that end, two corpora were built based on pesticide-related scientific documents in Brazilian Portuguese and English, which led to a total of 84 and 210 texts, respectively, representing the low-and high-resource languages in this study. We then assessed 6 methods for keyword extraction: Simple Maths, TF-IDF, YAKE, TextRank, MultipartiteRank, and KeyBERT. We relied on a multilingual contextual BERT embedding to retrieve corresponding pesticide names in the target language. Finetuning was also explored to improve the multilingual representation further. Moreover, we evaluated the use of large language models (LLMs) combined with the recent retrieval-augmented generation (RAG) framework. As a result, we found that the contextual approach, combined with fine-tuning, provided the best results, contributing to enhancing Pesticide Terminology Extraction in a multilingual scenario. © 2025, Brazilian Computing Society. All rights reserved.},
keywords = {Agriculture, BERT embedding, BERT embeddings, Cross-lingual, Embeddings, extraction, Food consumption, Keywords extraction, Labelings, Low resource languages, Multilingual extraction, Pesticides, Technical terms, Terminology, Word alignment},
pubstate = {published},
tppubtype = {article}
}
Agriculture plays a crucial role in Brazil’s economy. As the country intensifies its activities in the sector, the use of pesticides also increases. Hence, the risks associated with pesticide-laden food consumption have become a concern for chemistry researchers. An issue affecting regulatory standardization of pesticides in Brazil is the difficulty in translating pesticide names, particularly from English. For example, the word malathion can be translated from English to Portuguese as malatiom or malatião, resulting in inconsistent labeling. This issue extends to the broader problem of translating highly technical terms between languages, in particular for low-resource languages. In this work, we investigate terminological variation in the chemistry of organophosphorus pesticides. Our goal is to study strategies for domain-specific multilingual keyword extraction. To that end, two corpora were built based on pesticide-related scientific documents in Brazilian Portuguese and English, which led to a total of 84 and 210 texts, respectively, representing the low-and high-resource languages in this study. We then assessed 6 methods for keyword extraction: Simple Maths, TF-IDF, YAKE, TextRank, MultipartiteRank, and KeyBERT. We relied on a multilingual contextual BERT embedding to retrieve corresponding pesticide names in the target language. Finetuning was also explored to improve the multilingual representation further. Moreover, we evaluated the use of large language models (LLMs) combined with the recent retrieval-augmented generation (RAG) framework. As a result, we found that the contextual approach, combined with fine-tuning, provided the best results, contributing to enhancing Pesticide Terminology Extraction in a multilingual scenario. © 2025, Brazilian Computing Society. All rights reserved.
4.
Temmar, D. E.; Hamadene, A.; Nallaguntla, V.; Fursule, A.; Allili, M. S.; Kshirsagar, S.; Avila, A. R.
Phonetic Analysis of Real and Synthetic Speech Using HuBERT Embeddings: Perspectives for Deepfake Detection Proceedings Article
In: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., pp. 86–91, 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.).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Audio acoustics, Audio DeepFake Detection, Audio signal processing, Embeddings, Hu-BERT, KL-divergence, Linguistics, Phoneme and word Embedding, Phonetic analysis, Security systems, Self-Supervised Speech Representation, Speech analysis, Speech communication, Speech processing, Speech synthesis, Synthetic speech, Text to speech, Voice conversion
@inproceedings{temmarPhoneticAnalysisReal2025,
title = {Phonetic Analysis of Real and Synthetic Speech Using HuBERT Embeddings: Perspectives for Deepfake Detection},
author = {D. E. Temmar and A. Hamadene and V. Nallaguntla and A. Fursule and M. S. Allili and S. Kshirsagar and A. R. Avila},
url = {https://www.scopus.com/pages/publications/105033145913?origin=resultslist},
doi = {10.1109/SMC58881.2025.11343334},
isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
pages = {86–91},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The growing sophistication of speech generated by Artificial Intelligence (AI) has introduced new challenges in audio deepfake detection. Text-to-speech (TTS) and voice conversion (VC) technologies can now produce convincing synthetic speech with high quality and intelligibility. This poses a serious threat to voice biometric security systems, such as automatic speaker recognition. It also increases the risks associated to the spread of spoken disinformation, where synthetic voices can be used to disseminate malicious content. In this study, we conduct an analysis of real and synthetic speech at phonetic and word levels. For that, a parallel dataset comprising real and synthetic speech signals were developed based on a subset of the LibriSpeech ASR corpus. Synthetic speech samples were generated using two TTS and one VC systems: Coqui TTS, VITS TTS, and StarGANv2 VC. We adopted HuBERT, a self-supervised speech model, to extract speech embeddings. The motivation for using this model stems from its ability to recognize sound units corresponding to the so-called pseudo phonemes. Our analysis is based on the KL divergence (KLD) between the distributions of synthetic and real phonemes, which allowed us to rank synthetic phonemes based on their alignment with their real counterpart. We also trained several classifiers per phoneme to distinguish between real and synthetic samples. We then compute the correlations between KLD and accuracies per phoneme. Besides showing a list of phonemes that are more discriminative, our findings suggest that vowels correlate better with the classifiers' performance, suggesting that the KLD can be an indicator of the most distinguishable phonemes for deepfake detection. © 2025 IEEE.},
note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
keywords = {Artificial intelligence, Audio acoustics, Audio DeepFake Detection, Audio signal processing, Embeddings, Hu-BERT, KL-divergence, Linguistics, Phoneme and word Embedding, Phonetic analysis, Security systems, Self-Supervised Speech Representation, Speech analysis, Speech communication, Speech processing, Speech synthesis, Synthetic speech, Text to speech, Voice conversion},
pubstate = {published},
tppubtype = {inproceedings}
}
The growing sophistication of speech generated by Artificial Intelligence (AI) has introduced new challenges in audio deepfake detection. Text-to-speech (TTS) and voice conversion (VC) technologies can now produce convincing synthetic speech with high quality and intelligibility. This poses a serious threat to voice biometric security systems, such as automatic speaker recognition. It also increases the risks associated to the spread of spoken disinformation, where synthetic voices can be used to disseminate malicious content. In this study, we conduct an analysis of real and synthetic speech at phonetic and word levels. For that, a parallel dataset comprising real and synthetic speech signals were developed based on a subset of the LibriSpeech ASR corpus. Synthetic speech samples were generated using two TTS and one VC systems: Coqui TTS, VITS TTS, and StarGANv2 VC. We adopted HuBERT, a self-supervised speech model, to extract speech embeddings. The motivation for using this model stems from its ability to recognize sound units corresponding to the so-called pseudo phonemes. Our analysis is based on the KL divergence (KLD) between the distributions of synthetic and real phonemes, which allowed us to rank synthetic phonemes based on their alignment with their real counterpart. We also trained several classifiers per phoneme to distinguish between real and synthetic samples. We then compute the correlations between KLD and accuracies per phoneme. Besides showing a list of phonemes that are more discriminative, our findings suggest that vowels correlate better with the classifiers' performance, suggesting that the KLD can be an indicator of the most distinguishable phonemes for deepfake detection. © 2025 IEEE.
5.
Souza, J. V.; Amamou, H.; Chen, R.; Salari, E.; Gubelmann, R.; Niklaus, C.; Serpa, T.; Lima, M. M. F.; Pinto, P. T.; Kshirsagar, S.; Davoust, A.; Handschuh, S.; Avila, A. R.
Cross-Lingual Keyword Extraction for Pesticide Terminology in Brazilian Portuguese and English Journal Article
In: Journal of the Brazilian Computer Society, vol. 31, no. 1, pp. 973–990, 2025, ISSN: 01046500 (ISSN).
Abstract | Links | BibTeX | Tags: Agriculture, BERT embedding, BERT embeddings, Cross-lingual, Embeddings, extraction, Food consumption, Keywords extraction, Labelings, Low resource languages, Multilingual extraction, Pesticides, Technical terms, Terminology, Word alignment
@article{desouzaCrossLingualKeywordExtraction2025,
title = {Cross-Lingual Keyword Extraction for Pesticide Terminology in Brazilian Portuguese and English},
author = {J. V. Souza and H. Amamou and R. Chen and E. Salari and R. Gubelmann and C. Niklaus and T. Serpa and M. M. F. Lima and P. T. Pinto and S. Kshirsagar and A. Davoust and S. Handschuh and A. R. Avila},
url = {https://www.scopus.com/pages/publications/105019700300?origin=resultslist},
doi = {10.5753/jbcs.2025.5815},
issn = {01046500 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Journal of the Brazilian Computer Society},
volume = {31},
number = {1},
pages = {973–990},
publisher = {Brazilian Computing Society},
abstract = {Agriculture plays a crucial role in Brazil’s economy. As the country intensifies its activities in the sector, the use of pesticides also increases. Hence, the risks associated with pesticide-laden food consumption have become a concern for chemistry researchers. An issue affecting regulatory standardization of pesticides in Brazil is the difficulty in translating pesticide names, particularly from English. For example, the word malathion can be translated from English to Portuguese as malatiom or malatião, resulting in inconsistent labeling. This issue extends to the broader problem of translating highly technical terms between languages, in particular for low-resource languages. In this work, we investigate terminological variation in the chemistry of organophosphorus pesticides. Our goal is to study strategies for domain-specific multilingual keyword extraction. To that end, two corpora were built based on pesticide-related scientific documents in Brazilian Portuguese and English, which led to a total of 84 and 210 texts, respectively, representing the low-and high-resource languages in this study. We then assessed 6 methods for keyword extraction: Simple Maths, TF-IDF, YAKE, TextRank, MultipartiteRank, and KeyBERT. We relied on a multilingual contextual BERT embedding to retrieve corresponding pesticide names in the target language. Finetuning was also explored to improve the multilingual representation further. Moreover, we evaluated the use of large language models (LLMs) combined with the recent retrieval-augmented generation (RAG) framework. As a result, we found that the contextual approach, combined with fine-tuning, provided the best results, contributing to enhancing Pesticide Terminology Extraction in a multilingual scenario. © 2025, Brazilian Computing Society. All rights reserved.},
keywords = {Agriculture, BERT embedding, BERT embeddings, Cross-lingual, Embeddings, extraction, Food consumption, Keywords extraction, Labelings, Low resource languages, Multilingual extraction, Pesticides, Technical terms, Terminology, Word alignment},
pubstate = {published},
tppubtype = {article}
}
Agriculture plays a crucial role in Brazil’s economy. As the country intensifies its activities in the sector, the use of pesticides also increases. Hence, the risks associated with pesticide-laden food consumption have become a concern for chemistry researchers. An issue affecting regulatory standardization of pesticides in Brazil is the difficulty in translating pesticide names, particularly from English. For example, the word malathion can be translated from English to Portuguese as malatiom or malatião, resulting in inconsistent labeling. This issue extends to the broader problem of translating highly technical terms between languages, in particular for low-resource languages. In this work, we investigate terminological variation in the chemistry of organophosphorus pesticides. Our goal is to study strategies for domain-specific multilingual keyword extraction. To that end, two corpora were built based on pesticide-related scientific documents in Brazilian Portuguese and English, which led to a total of 84 and 210 texts, respectively, representing the low-and high-resource languages in this study. We then assessed 6 methods for keyword extraction: Simple Maths, TF-IDF, YAKE, TextRank, MultipartiteRank, and KeyBERT. We relied on a multilingual contextual BERT embedding to retrieve corresponding pesticide names in the target language. Finetuning was also explored to improve the multilingual representation further. Moreover, we evaluated the use of large language models (LLMs) combined with the recent retrieval-augmented generation (RAG) framework. As a result, we found that the contextual approach, combined with fine-tuning, provided the best results, contributing to enhancing Pesticide Terminology Extraction in a multilingual scenario. © 2025, Brazilian Computing Society. All rights reserved.
6.
Allaoui, M.; Hedjam, R.; Bouanane, K.; Allili, M. S.; Kherfi, M. L.; Belhaouari, S. B.
Exploring non-negativity for improved manifold embedding: Application to t-SNE Journal Article
In: Knowledge-Based Systems, vol. 330, 2025, ISSN: 09507051 (ISSN).
Abstract | Links | BibTeX | Tags: Dimensionality reduction, Embedding technique, Embeddings, Gradient methods, Gradient-descent, Manifold embedding, Matrix algebra, Non-negative matrix factorization, Non-negativity, Nonnegative matrix factorization, Nonnegativity constraints, Performance, T-SNE
@article{allaouiExploringNonnegativityImproved2025,
title = {Exploring non-negativity for improved manifold embedding: Application to t-SNE},
author = {M. Allaoui and R. Hedjam and K. Bouanane and M. S. Allili and M. L. Kherfi and S. B. Belhaouari},
url = {https://www.scopus.com/pages/publications/105018098090?origin=resultslist},
doi = {10.1016/j.knosys.2025.114547},
issn = {09507051 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Knowledge-Based Systems},
volume = {330},
publisher = {Elsevier B.V.},
abstract = {Drawing inspiration from Non-negative Matrix Factorization (NMF), this paper explores the potential of incorporating non-negativity constraints into embedding techniques, with a focus on t-SNE as an application. Specifically, we investigate the following questions: Can enforcing non-negativity in the embedding space enhance interpretability and improve the quality of embedded data? By prioritizing non-negativity, can embedding methods achieve better performance and more meaningful representations? Additionally, does enforcing non-negativity in the embedded space help preserve both the local and global structure of data in the manifold, leading to more accurate and interpretable embeddings? In this work, we could show both objectively and subjectively how enforcing t-SNE to leverage the non-negativity of the data addresses the raised questions. To achieve this, we introduced a novel approach to transforming the additive update rule of the gradient descent used by t-SNE to a multiplicative counterpart to enforce the non-negativity in the embedded space. However, grappling with full non-negativity in the gradient descent formula presents challenges, prompting our focus solely on the (yi−yj) term, resulting in a semi-non-negative t-SNE algorithm, shortly named SN-tSNE. Nevertheless, experimental findings substantiate the significant impact of the proposed update rule on the performance and efficacy of the SN-tSNE algorithm. Furthermore, additional experiments are performed to compare SN-tSNE with its precursor t-SNE, as well as the competitive embedding technique UMAP, alongside other relevant embedding and dimensionality reduction models like NMF. The source code of SN-tSNE is available on GitHub (https://github.com/M-Allaoui/SN-tSNE.git). © 2025},
keywords = {Dimensionality reduction, Embedding technique, Embeddings, Gradient methods, Gradient-descent, Manifold embedding, Matrix algebra, Non-negative matrix factorization, Non-negativity, Nonnegative matrix factorization, Nonnegativity constraints, Performance, T-SNE},
pubstate = {published},
tppubtype = {article}
}
Drawing inspiration from Non-negative Matrix Factorization (NMF), this paper explores the potential of incorporating non-negativity constraints into embedding techniques, with a focus on t-SNE as an application. Specifically, we investigate the following questions: Can enforcing non-negativity in the embedding space enhance interpretability and improve the quality of embedded data? By prioritizing non-negativity, can embedding methods achieve better performance and more meaningful representations? Additionally, does enforcing non-negativity in the embedded space help preserve both the local and global structure of data in the manifold, leading to more accurate and interpretable embeddings? In this work, we could show both objectively and subjectively how enforcing t-SNE to leverage the non-negativity of the data addresses the raised questions. To achieve this, we introduced a novel approach to transforming the additive update rule of the gradient descent used by t-SNE to a multiplicative counterpart to enforce the non-negativity in the embedded space. However, grappling with full non-negativity in the gradient descent formula presents challenges, prompting our focus solely on the (yi−yj) term, resulting in a semi-non-negative t-SNE algorithm, shortly named SN-tSNE. Nevertheless, experimental findings substantiate the significant impact of the proposed update rule on the performance and efficacy of the SN-tSNE algorithm. Furthermore, additional experiments are performed to compare SN-tSNE with its precursor t-SNE, as well as the competitive embedding technique UMAP, alongside other relevant embedding and dimensionality reduction models like NMF. The source code of SN-tSNE is available on GitHub (https://github.com/M-Allaoui/SN-tSNE.git). © 2025
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 Journal Article
In: IEEE Open Journal of the Communications Society, vol. 6, pp. 1129–1148, 2025, ISSN: 2644125X (ISSN).
Abstract | Links | BibTeX | Tags: 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.
Zhu, Y.; Falk, T.
WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model Journal Article
In: IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 9, pp. 6353–6365, 2025, ISSN: 21682194 (ISSN).
Abstract | Links | BibTeX | Tags: Agnostic, area under the curve, article, artificial neural network, asthma, autoencoder, Benchmarking, breathing, chronic obstructive lung disease, Computer-Assisted, controlled study, convolutional neural network, coronavirus disease 2019, Cross-domain, Databases, Diagnosis, Diagnostic, Diagnostic model, diagnostic test accuracy study, diagnostics, Differential privacy, Dynamics, dysarthria, Electronic health record, embedding, Embeddings, Factual, factual database, Generalizability, Health embedding, Health embeddings, Health monitoring, human, Humans, Machine learning, malignant neoplasm, model, Pathological speech, pathophysiology, physiology, pneumonia, Privacy, Privacy preserving, privacy preserving speech health diagnostic model, privacy-preserving, Privacy-preserving techniques, receiver operating characteristic, short time Fourier transform, Signal processing, speech, speech articulation, speech disorder, Speech Disorders, State of the art, temporal representation encoder, training, waveform
@article{zhuWavRxDiseaseAgnosticGeneralizable2025,
title = {WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model},
author = {Y. Zhu and T. Falk},
url = {https://www.scopus.com/pages/publications/85203439930?origin=resultslist},
doi = {10.1109/JBHI.2024.3454550},
issn = {21682194 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {29},
number = {9},
pages = {6353–6365},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizability across datasets. Furthermore, concerns have been raised recently towards the leakage of speaker identity from health embeddings. To mitigate these limitations, we propose WavRx, a speech health diagnostics model that captures the respiration and articulation related dynamics from a universal speech representation. Our in-domain and cross-domain experiments on six pathological speech datasets demonstrate WavRx as a new state-of-the-art health diagnostic model. Furthermore, we show that the amount of speaker identity entailed in the WavRx health embeddings is significantly reduced without extra guidance during training. An in-depth analysis of the model was performed, thus providing physiological interpretation of its improved generalizability and privacy-preserving ability. © 2013 IEEE.},
keywords = {Agnostic, area under the curve, article, artificial neural network, asthma, autoencoder, Benchmarking, breathing, chronic obstructive lung disease, Computer-Assisted, controlled study, convolutional neural network, coronavirus disease 2019, Cross-domain, Databases, Diagnosis, Diagnostic, Diagnostic model, diagnostic test accuracy study, diagnostics, Differential privacy, Dynamics, dysarthria, Electronic health record, embedding, Embeddings, Factual, factual database, Generalizability, Health embedding, Health embeddings, Health monitoring, human, Humans, Machine learning, malignant neoplasm, model, Pathological speech, pathophysiology, physiology, pneumonia, Privacy, Privacy preserving, privacy preserving speech health diagnostic model, privacy-preserving, Privacy-preserving techniques, receiver operating characteristic, short time Fourier transform, Signal processing, speech, speech articulation, speech disorder, Speech Disorders, State of the art, temporal representation encoder, training, waveform},
pubstate = {published},
tppubtype = {article}
}
Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizability across datasets. Furthermore, concerns have been raised recently towards the leakage of speaker identity from health embeddings. To mitigate these limitations, we propose WavRx, a speech health diagnostics model that captures the respiration and articulation related dynamics from a universal speech representation. Our in-domain and cross-domain experiments on six pathological speech datasets demonstrate WavRx as a new state-of-the-art health diagnostic model. Furthermore, we show that the amount of speaker identity entailed in the WavRx health embeddings is significantly reduced without extra guidance during training. An in-depth analysis of the model was performed, thus providing physiological interpretation of its improved generalizability and privacy-preserving ability. © 2013 IEEE.
9.
Allaoui, M.; Hedjam, R.; Bouanane, K.; Allili, M. S.; Kherfi, M. L.; Belhaouari, S. B.
Exploring non-negativity for improved manifold embedding: Application to t-SNE Journal Article
In: Knowledge-Based Systems, vol. 330, 2025, ISSN: 09507051 (ISSN).
Abstract | Links | BibTeX | Tags: Dimensionality reduction, Embedding technique, Embeddings, Gradient methods, Gradient-descent, Manifold embedding, Matrix algebra, Non-negative matrix factorization, Non-negativity, Nonnegative matrix factorization, Nonnegativity constraints, Performance, T-SNE
@article{allaouiExploringNonnegativityImproved2025a,
title = {Exploring non-negativity for improved manifold embedding: Application to t-SNE},
author = {M. Allaoui and R. Hedjam and K. Bouanane and M. S. Allili and M. L. Kherfi and S. B. Belhaouari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018098090&doi=10.1016%2Fj.knosys.2025.114547&partnerID=40&md5=237540c38a928146d589b96cd6888547},
doi = {10.1016/j.knosys.2025.114547},
issn = {09507051 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Knowledge-Based Systems},
volume = {330},
abstract = {Drawing inspiration from Non-negative Matrix Factorization (NMF), this paper explores the potential of incorporating non-negativity constraints into embedding techniques, with a focus on t-SNE as an application. Specifically, we investigate the following questions: Can enforcing non-negativity in the embedding space enhance interpretability and improve the quality of embedded data? By prioritizing non-negativity, can embedding methods achieve better performance and more meaningful representations? Additionally, does enforcing non-negativity in the embedded space help preserve both the local and global structure of data in the manifold, leading to more accurate and interpretable embeddings? In this work, we could show both objectively and subjectively how enforcing t-SNE to leverage the non-negativity of the data addresses the raised questions. To achieve this, we introduced a novel approach to transforming the additive update rule of the gradient descent used by t-SNE to a multiplicative counterpart to enforce the non-negativity in the embedded space. However, grappling with full non-negativity in the gradient descent formula presents challenges, prompting our focus solely on the (yi−yj) term, resulting in a semi-non-negative t-SNE algorithm, shortly named SN-tSNE. Nevertheless, experimental findings substantiate the significant impact of the proposed update rule on the performance and efficacy of the SN-tSNE algorithm. Furthermore, additional experiments are performed to compare SN-tSNE with its precursor t-SNE, as well as the competitive embedding technique UMAP, alongside other relevant embedding and dimensionality reduction models like NMF. The source code of SN-tSNE is available on GitHub (https://github.com/M-Allaoui/SN-tSNE.git). © 2025},
keywords = {Dimensionality reduction, Embedding technique, Embeddings, Gradient methods, Gradient-descent, Manifold embedding, Matrix algebra, Non-negative matrix factorization, Non-negativity, Nonnegative matrix factorization, Nonnegativity constraints, Performance, T-SNE},
pubstate = {published},
tppubtype = {article}
}
Drawing inspiration from Non-negative Matrix Factorization (NMF), this paper explores the potential of incorporating non-negativity constraints into embedding techniques, with a focus on t-SNE as an application. Specifically, we investigate the following questions: Can enforcing non-negativity in the embedding space enhance interpretability and improve the quality of embedded data? By prioritizing non-negativity, can embedding methods achieve better performance and more meaningful representations? Additionally, does enforcing non-negativity in the embedded space help preserve both the local and global structure of data in the manifold, leading to more accurate and interpretable embeddings? In this work, we could show both objectively and subjectively how enforcing t-SNE to leverage the non-negativity of the data addresses the raised questions. To achieve this, we introduced a novel approach to transforming the additive update rule of the gradient descent used by t-SNE to a multiplicative counterpart to enforce the non-negativity in the embedded space. However, grappling with full non-negativity in the gradient descent formula presents challenges, prompting our focus solely on the (yi−yj) term, resulting in a semi-non-negative t-SNE algorithm, shortly named SN-tSNE. Nevertheless, experimental findings substantiate the significant impact of the proposed update rule on the performance and efficacy of the SN-tSNE algorithm. Furthermore, additional experiments are performed to compare SN-tSNE with its precursor t-SNE, as well as the competitive embedding technique UMAP, alongside other relevant embedding and dimensionality reduction models like NMF. The source code of SN-tSNE is available on GitHub (https://github.com/M-Allaoui/SN-tSNE.git). © 2025
10.
Souza, J. V.; Amamou, H.; Chen, R.; Salari, E.; Gubelmann, R.; Niklaus, C.; Serpa, T.; Lima, M. M. F.; Pinto, P. T.; Kshirsagar, S.; Davoust, A.; Handschuh, S.; Avila, A. R.
Cross-Lingual Keyword Extraction for Pesticide Terminology in Brazilian Portuguese and English Journal Article
In: Journal of the Brazilian Computer Society, vol. 31, no. 1, pp. 973–990, 2025, ISSN: 01046500 (ISSN).
Abstract | Links | BibTeX | Tags: Agriculture, BERT embedding, BERT embeddings, Cross-lingual, Embeddings, extraction, Food consumption, Keywords extraction, Labelings, Low resource languages, Multilingual extraction, Pesticides, Technical terms, Terminology, Word alignment
@article{desouzaCrossLingualKeywordExtraction2025a,
title = {Cross-Lingual Keyword Extraction for Pesticide Terminology in Brazilian Portuguese and English},
author = {J. V. Souza and H. Amamou and R. Chen and E. Salari and R. Gubelmann and C. Niklaus and T. Serpa and M. M. F. Lima and P. T. Pinto and S. Kshirsagar and A. Davoust and S. Handschuh and A. R. Avila},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105019700300&doi=10.5753%2Fjbcs.2025.5815&partnerID=40&md5=85ee75baf4550666a307310cd04d1c83},
doi = {10.5753/jbcs.2025.5815},
issn = {01046500 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Journal of the Brazilian Computer Society},
volume = {31},
number = {1},
pages = {973–990},
abstract = {Agriculture plays a crucial role in Brazil’s economy. As the country intensifies its activities in the sector, the use of pesticides also increases. Hence, the risks associated with pesticide-laden food consumption have become a concern for chemistry researchers. An issue affecting regulatory standardization of pesticides in Brazil is the difficulty in translating pesticide names, particularly from English. For example, the word malathion can be translated from English to Portuguese as malatiom or malatião, resulting in inconsistent labeling. This issue extends to the broader problem of translating highly technical terms between languages, in particular for low-resource languages. In this work, we investigate terminological variation in the chemistry of organophosphorus pesticides. Our goal is to study strategies for domain-specific multilingual keyword extraction. To that end, two corpora were built based on pesticide-related scientific documents in Brazilian Portuguese and English, which led to a total of 84 and 210 texts, respectively, representing the low-and high-resource languages in this study. We then assessed 6 methods for keyword extraction: Simple Maths, TF-IDF, YAKE, TextRank, MultipartiteRank, and KeyBERT. We relied on a multilingual contextual BERT embedding to retrieve corresponding pesticide names in the target language. Finetuning was also explored to improve the multilingual representation further. Moreover, we evaluated the use of large language models (LLMs) combined with the recent retrieval-augmented generation (RAG) framework. As a result, we found that the contextual approach, combined with fine-tuning, provided the best results, contributing to enhancing Pesticide Terminology Extraction in a multilingual scenario. © 2025, Brazilian Computing Society. All rights reserved.},
keywords = {Agriculture, BERT embedding, BERT embeddings, Cross-lingual, Embeddings, extraction, Food consumption, Keywords extraction, Labelings, Low resource languages, Multilingual extraction, Pesticides, Technical terms, Terminology, Word alignment},
pubstate = {published},
tppubtype = {article}
}
Agriculture plays a crucial role in Brazil’s economy. As the country intensifies its activities in the sector, the use of pesticides also increases. Hence, the risks associated with pesticide-laden food consumption have become a concern for chemistry researchers. An issue affecting regulatory standardization of pesticides in Brazil is the difficulty in translating pesticide names, particularly from English. For example, the word malathion can be translated from English to Portuguese as malatiom or malatião, resulting in inconsistent labeling. This issue extends to the broader problem of translating highly technical terms between languages, in particular for low-resource languages. In this work, we investigate terminological variation in the chemistry of organophosphorus pesticides. Our goal is to study strategies for domain-specific multilingual keyword extraction. To that end, two corpora were built based on pesticide-related scientific documents in Brazilian Portuguese and English, which led to a total of 84 and 210 texts, respectively, representing the low-and high-resource languages in this study. We then assessed 6 methods for keyword extraction: Simple Maths, TF-IDF, YAKE, TextRank, MultipartiteRank, and KeyBERT. We relied on a multilingual contextual BERT embedding to retrieve corresponding pesticide names in the target language. Finetuning was also explored to improve the multilingual representation further. Moreover, we evaluated the use of large language models (LLMs) combined with the recent retrieval-augmented generation (RAG) framework. As a result, we found that the contextual approach, combined with fine-tuning, provided the best results, contributing to enhancing Pesticide Terminology Extraction in a multilingual scenario. © 2025, Brazilian Computing Society. All rights reserved.