Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Ameyoud, S. Mohamed; Allili, M. Saïd
Multi-modal malware classification with hierarchical consistency and saliency-constrained adversarial training Article de journal
Dans: Journal of Information Security and Applications, vol. 99, 2026, ISSN: 22142134 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Adversarial training, Capability of detection, Classification (of information), Convolution, convolutional neural network, Convolutional neural networks, Detection system, Hierarchical consistency, Hierarchical systems, Malware, Malware classification, Malware classifications, Malware families, Malwares, Multi-modal, Multi-modal learning, Semantics, Vision transformer, Vision transformers
@article{mohamed_ameyoud_multi-modal_2026,
title = {Multi-modal malware classification with hierarchical consistency and saliency-constrained adversarial training},
author = {S. Mohamed Ameyoud and M. Saïd Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105031186108&doi=10.1016%2Fj.jisa.2026.104429&partnerID=40&md5=2425da4ab40f9043ba4e67d223a1bdd9},
doi = {10.1016/j.jisa.2026.104429},
issn = {22142134 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Journal of Information Security and Applications},
volume = {99},
abstract = {The increasing complexity of malware, including polymorphic, obfuscated, and adversarial variants, continues to outpace the capabilities of detection systems. Here, we introduce a robust multi-modal hierarchical framework that jointly leverages visual and code-level semantics to enhance malware family and type classification. Our architecture fuses convolutional and transformer-based encoders to extract complementary representations from raw malware binaries and decompiled control-flow functions, enabling a rich, cross-modal understanding of malicious behavior. The classification pipeline follows a two-stage hierarchical protocol, where the predicted malware type informs the family-level classification. This enforces ontological consistency between type and family prediction levels. To further bolster robustness against adversarial and obfuscated malware, we integrate a novel adversarial training strategy that generates plausible perturbations guided by attention distributions. Evaluation on multiple large-scale benchmarks including BODMAS, Malimg, Microsoft BIG 2015, and a curated set of from MalwareBazaar, demonstrate that our framework consistently outperforms state-of-the-art baselines, including ResNet, Swin Transformer, and MalBERTv2, across both malware type and family prediction tasks. Notably, our model exhibits outstanding generalization to unpacked, obfuscated, and previously unseen samples, with minimal performance degradation. It achieves accuracy gains of +3-6% over leading methods and exhibits superior resilience under adversarial threat models. These results highlight the effectiveness of hierarchical conditioning, adversarial robustness, and multi-modal fusion in tackling the evolving landscape of malware. The proposed framework thus offers a scalable and generalizable approach for next-generation malware classification in real-world cybersecurity environments. © 2026 Elsevier Ltd.},
keywords = {Adversarial training, Capability of detection, Classification (of information), Convolution, convolutional neural network, Convolutional neural networks, Detection system, Hierarchical consistency, Hierarchical systems, Malware, Malware classification, Malware classifications, Malware families, Malwares, Multi-modal, Multi-modal learning, Semantics, Vision transformer, Vision transformers},
pubstate = {published},
tppubtype = {article}
}
The increasing complexity of malware, including polymorphic, obfuscated, and adversarial variants, continues to outpace the capabilities of detection systems. Here, we introduce a robust multi-modal hierarchical framework that jointly leverages visual and code-level semantics to enhance malware family and type classification. Our architecture fuses convolutional and transformer-based encoders to extract complementary representations from raw malware binaries and decompiled control-flow functions, enabling a rich, cross-modal understanding of malicious behavior. The classification pipeline follows a two-stage hierarchical protocol, where the predicted malware type informs the family-level classification. This enforces ontological consistency between type and family prediction levels. To further bolster robustness against adversarial and obfuscated malware, we integrate a novel adversarial training strategy that generates plausible perturbations guided by attention distributions. Evaluation on multiple large-scale benchmarks including BODMAS, Malimg, Microsoft BIG 2015, and a curated set of from MalwareBazaar, demonstrate that our framework consistently outperforms state-of-the-art baselines, including ResNet, Swin Transformer, and MalBERTv2, across both malware type and family prediction tasks. Notably, our model exhibits outstanding generalization to unpacked, obfuscated, and previously unseen samples, with minimal performance degradation. It achieves accuracy gains of +3-6% over leading methods and exhibits superior resilience under adversarial threat models. These results highlight the effectiveness of hierarchical conditioning, adversarial robustness, and multi-modal fusion in tackling the evolving landscape of malware. The proposed framework thus offers a scalable and generalizable approach for next-generation malware classification in real-world cybersecurity environments. © 2026 Elsevier Ltd.
2.
Ameyoud, S. Mohamed; Allili, M. Saïd
Multi-modal malware classification with hierarchical consistency and saliency-constrained adversarial training Article de journal
Dans: Journal of Information Security and Applications, vol. 99, 2026, ISSN: 22142134 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Adversarial training, Capability of detection, Classification (of information), Convolution, convolutional neural network, Convolutional neural networks, Detection system, Hierarchical consistency, Hierarchical systems, Malware, Malware classification, Malware classifications, Malware families, Malwares, Multi-modal, Multi-modal learning, Semantics, Vision transformer, Vision transformers
@article{mohamedameyoudMultimodalMalwareClassification2026,
title = {Multi-modal malware classification with hierarchical consistency and saliency-constrained adversarial training},
author = {S. Mohamed Ameyoud and M. Saïd Allili},
url = {https://www.scopus.com/pages/publications/105031186108?origin=resultslist},
doi = {10.1016/j.jisa.2026.104429},
issn = {22142134 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Journal of Information Security and Applications},
volume = {99},
publisher = {Elsevier Ltd},
abstract = {The increasing complexity of malware, including polymorphic, obfuscated, and adversarial variants, continues to outpace the capabilities of detection systems. Here, we introduce a robust multi-modal hierarchical framework that jointly leverages visual and code-level semantics to enhance malware family and type classification. Our architecture fuses convolutional and transformer-based encoders to extract complementary representations from raw malware binaries and decompiled control-flow functions, enabling a rich, cross-modal understanding of malicious behavior. The classification pipeline follows a two-stage hierarchical protocol, where the predicted malware type informs the family-level classification. This enforces ontological consistency between type and family prediction levels. To further bolster robustness against adversarial and obfuscated malware, we integrate a novel adversarial training strategy that generates plausible perturbations guided by attention distributions. Evaluation on multiple large-scale benchmarks including BODMAS, Malimg, Microsoft BIG 2015, and a curated set of from MalwareBazaar, demonstrate that our framework consistently outperforms state-of-the-art baselines, including ResNet, Swin Transformer, and MalBERTv2, across both malware type and family prediction tasks. Notably, our model exhibits outstanding generalization to unpacked, obfuscated, and previously unseen samples, with minimal performance degradation. It achieves accuracy gains of +3-6% over leading methods and exhibits superior resilience under adversarial threat models. These results highlight the effectiveness of hierarchical conditioning, adversarial robustness, and multi-modal fusion in tackling the evolving landscape of malware. The proposed framework thus offers a scalable and generalizable approach for next-generation malware classification in real-world cybersecurity environments. © 2026 Elsevier Ltd.},
keywords = {Adversarial training, Capability of detection, Classification (of information), Convolution, convolutional neural network, Convolutional neural networks, Detection system, Hierarchical consistency, Hierarchical systems, Malware, Malware classification, Malware classifications, Malware families, Malwares, Multi-modal, Multi-modal learning, Semantics, Vision transformer, Vision transformers},
pubstate = {published},
tppubtype = {article}
}
The increasing complexity of malware, including polymorphic, obfuscated, and adversarial variants, continues to outpace the capabilities of detection systems. Here, we introduce a robust multi-modal hierarchical framework that jointly leverages visual and code-level semantics to enhance malware family and type classification. Our architecture fuses convolutional and transformer-based encoders to extract complementary representations from raw malware binaries and decompiled control-flow functions, enabling a rich, cross-modal understanding of malicious behavior. The classification pipeline follows a two-stage hierarchical protocol, where the predicted malware type informs the family-level classification. This enforces ontological consistency between type and family prediction levels. To further bolster robustness against adversarial and obfuscated malware, we integrate a novel adversarial training strategy that generates plausible perturbations guided by attention distributions. Evaluation on multiple large-scale benchmarks including BODMAS, Malimg, Microsoft BIG 2015, and a curated set of from MalwareBazaar, demonstrate that our framework consistently outperforms state-of-the-art baselines, including ResNet, Swin Transformer, and MalBERTv2, across both malware type and family prediction tasks. Notably, our model exhibits outstanding generalization to unpacked, obfuscated, and previously unseen samples, with minimal performance degradation. It achieves accuracy gains of +3-6% over leading methods and exhibits superior resilience under adversarial threat models. These results highlight the effectiveness of hierarchical conditioning, adversarial robustness, and multi-modal fusion in tackling the evolving landscape of malware. The proposed framework thus offers a scalable and generalizable approach for next-generation malware classification in real-world cybersecurity environments. © 2026 Elsevier Ltd.
3.
Ameyoud, S. Mohamed; Allili, M. Saïd
Multi-modal malware classification with hierarchical consistency and saliency-constrained adversarial training Article de journal
Dans: Journal of Information Security and Applications, vol. 99, 2026, ISSN: 22142134 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Adversarial training, Capability of detection, Classification (of information), Convolution, convolutional neural network, Convolutional neural networks, Detection system, Hierarchical consistency, Hierarchical systems, Malware, Malware classification, Malware classifications, Malware families, Malwares, Multi-modal, Multi-modal learning, Semantics, Vision transformer, Vision transformers
@article{mohamedameyoudMultimodalMalwareClassification2026a,
title = {Multi-modal malware classification with hierarchical consistency and saliency-constrained adversarial training},
author = {S. Mohamed Ameyoud and M. Saïd Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105031186108&doi=10.1016%2Fj.jisa.2026.104429&partnerID=40&md5=2425da4ab40f9043ba4e67d223a1bdd9},
doi = {10.1016/j.jisa.2026.104429},
issn = {22142134 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Journal of Information Security and Applications},
volume = {99},
abstract = {The increasing complexity of malware, including polymorphic, obfuscated, and adversarial variants, continues to outpace the capabilities of detection systems. Here, we introduce a robust multi-modal hierarchical framework that jointly leverages visual and code-level semantics to enhance malware family and type classification. Our architecture fuses convolutional and transformer-based encoders to extract complementary representations from raw malware binaries and decompiled control-flow functions, enabling a rich, cross-modal understanding of malicious behavior. The classification pipeline follows a two-stage hierarchical protocol, where the predicted malware type informs the family-level classification. This enforces ontological consistency between type and family prediction levels. To further bolster robustness against adversarial and obfuscated malware, we integrate a novel adversarial training strategy that generates plausible perturbations guided by attention distributions. Evaluation on multiple large-scale benchmarks including BODMAS, Malimg, Microsoft BIG 2015, and a curated set of from MalwareBazaar, demonstrate that our framework consistently outperforms state-of-the-art baselines, including ResNet, Swin Transformer, and MalBERTv2, across both malware type and family prediction tasks. Notably, our model exhibits outstanding generalization to unpacked, obfuscated, and previously unseen samples, with minimal performance degradation. It achieves accuracy gains of +3-6% over leading methods and exhibits superior resilience under adversarial threat models. These results highlight the effectiveness of hierarchical conditioning, adversarial robustness, and multi-modal fusion in tackling the evolving landscape of malware. The proposed framework thus offers a scalable and generalizable approach for next-generation malware classification in real-world cybersecurity environments. © 2026 Elsevier Ltd.},
keywords = {Adversarial training, Capability of detection, Classification (of information), Convolution, convolutional neural network, Convolutional neural networks, Detection system, Hierarchical consistency, Hierarchical systems, Malware, Malware classification, Malware classifications, Malware families, Malwares, Multi-modal, Multi-modal learning, Semantics, Vision transformer, Vision transformers},
pubstate = {published},
tppubtype = {article}
}
The increasing complexity of malware, including polymorphic, obfuscated, and adversarial variants, continues to outpace the capabilities of detection systems. Here, we introduce a robust multi-modal hierarchical framework that jointly leverages visual and code-level semantics to enhance malware family and type classification. Our architecture fuses convolutional and transformer-based encoders to extract complementary representations from raw malware binaries and decompiled control-flow functions, enabling a rich, cross-modal understanding of malicious behavior. The classification pipeline follows a two-stage hierarchical protocol, where the predicted malware type informs the family-level classification. This enforces ontological consistency between type and family prediction levels. To further bolster robustness against adversarial and obfuscated malware, we integrate a novel adversarial training strategy that generates plausible perturbations guided by attention distributions. Evaluation on multiple large-scale benchmarks including BODMAS, Malimg, Microsoft BIG 2015, and a curated set of from MalwareBazaar, demonstrate that our framework consistently outperforms state-of-the-art baselines, including ResNet, Swin Transformer, and MalBERTv2, across both malware type and family prediction tasks. Notably, our model exhibits outstanding generalization to unpacked, obfuscated, and previously unseen samples, with minimal performance degradation. It achieves accuracy gains of +3-6% over leading methods and exhibits superior resilience under adversarial threat models. These results highlight the effectiveness of hierarchical conditioning, adversarial robustness, and multi-modal fusion in tackling the evolving landscape of malware. The proposed framework thus offers a scalable and generalizable approach for next-generation malware classification in real-world cybersecurity environments. © 2026 Elsevier Ltd.
4.
Boudra, N.; Elhajjout, A.; Moudoud, H.; Oujaoura, M.; Jarir, Z.; Houda, Z. A. El
Toward Lightweight IoC Extraction in IoT: The Role of Small Language Models Article d'actes
Dans: Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833157801-5 (ISBN), (Journal Abbreviation: Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY).
Résumé | Liens | BibTeX | Étiquettes: Artificial intelligence, Cyber threats, Data-source, Edge Computing, extraction, Indicator of compromize, Indicators of Compromise, Information retrieval, Information Security, IoT Security, Language model, Malware, Natural languages, Network security, Program processors, Resource Constraint, Resource Constraints, Semantics, Small language model, Small Language Models, Threat Intelligence
@inproceedings{boudraLightweightIoCExtraction2025,
title = {Toward Lightweight IoC Extraction in IoT: The Role of Small Language Models},
author = {N. Boudra and A. Elhajjout and H. Moudoud and M. Oujaoura and Z. Jarir and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105037622873?origin=resultslist},
doi = {10.1109/SmartAgriSuSY68475.2025.11466843},
isbn = {979-833157801-5 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The proliferation of cyber threats necessitates rapid and accurate extraction of Indicators of Compromise (IoCs) from diverse security data sources. While Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language processing and information extraction tasks, their deployment on resource-constrained IoT devices remains challenging due to computational and memory requirements. This paper investigates whether Small Language Models (SLMs) can serve as effective alternatives to LLMs for IoC extraction in IoT environments with limited resources. We present a novel direct LLM-based IoC extraction system leveraging context memory mechanisms for document-wide semantic understanding, specifically designed for deployment on edge computing infrastructure with consumer-grade hardware. Our experimental setup utilizes an RTX 4080 GPU and Ryzen 7 7700X processor running the Ollama framework with GPT-OSS:20B model to evaluate the feasibility of using smaller models instead of resource-intensive LLMs for security tasks. Evaluated on 9 diverse threat intelligence reports spanning different malware families and attack campaigns, the system achieved an average F1 score of 0.62, precision of 0.54, recall of 0.79, and accuracy of 0.85, demonstrating that SLMs can achieve acceptable accuracy levels for IoC extraction while operating within the computational constraints typical of IoT edge deployments. The results suggest that smaller models may provide viable alternatives to large language models for distributed threat intelligence processing in resource-limited environments. © 2025 IEEE.},
note = {Journal Abbreviation: Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY},
keywords = {Artificial intelligence, Cyber threats, Data-source, Edge Computing, extraction, Indicator of compromize, Indicators of Compromise, Information retrieval, Information Security, IoT Security, Language model, Malware, Natural languages, Network security, Program processors, Resource Constraint, Resource Constraints, Semantics, Small language model, Small Language Models, Threat Intelligence},
pubstate = {published},
tppubtype = {inproceedings}
}
The proliferation of cyber threats necessitates rapid and accurate extraction of Indicators of Compromise (IoCs) from diverse security data sources. While Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language processing and information extraction tasks, their deployment on resource-constrained IoT devices remains challenging due to computational and memory requirements. This paper investigates whether Small Language Models (SLMs) can serve as effective alternatives to LLMs for IoC extraction in IoT environments with limited resources. We present a novel direct LLM-based IoC extraction system leveraging context memory mechanisms for document-wide semantic understanding, specifically designed for deployment on edge computing infrastructure with consumer-grade hardware. Our experimental setup utilizes an RTX 4080 GPU and Ryzen 7 7700X processor running the Ollama framework with GPT-OSS:20B model to evaluate the feasibility of using smaller models instead of resource-intensive LLMs for security tasks. Evaluated on 9 diverse threat intelligence reports spanning different malware families and attack campaigns, the system achieved an average F1 score of 0.62, precision of 0.54, recall of 0.79, and accuracy of 0.85, demonstrating that SLMs can achieve acceptable accuracy levels for IoC extraction while operating within the computational constraints typical of IoT edge deployments. The results suggest that smaller models may provide viable alternatives to large language models for distributed threat intelligence processing in resource-limited environments. © 2025 IEEE.