| DOI | Trouver le DOI : https://doi.org/10.1109/ISNCC66965.2025.11250444 |
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| Auteur | Rechercher : Iqbal, Shahrear1Identifiant ORCID : https://orcid.org/0000-0001-7819-5715; Rechercher : Khanzadeh, Sourena2; Rechercher : Pinto Neto, Euclides Carlos1; Rechercher : Buffett, Scott1; Rechercher : Sultana, Madeena3; Rechercher : Taylor, Adrian2 |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
- Toronto Metropolitan University
- Recherche et développement pour la défense Canada
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| Format | Texte, Article |
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| Conférence | 2025 International Symposium on Networks, Computers and Communications (ISNCC), October 27-29, 2025, Paris, France |
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| Sujet | auxiliary knowledge; cybersecurity; knowledge-infused learning (KIL); machine learning |
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| Résumé | Malicious activities are becoming more complex and difficult to detect, leading to a need for advanced solutions. Machine Learning (ML) presents several success cases across multiple industries and in cybersecurity, ML has demonstrated promising performance in the detection and classification of malicious activities. However, there are still critical limitations that prevent their wide adoption in cybersecurity operations (e.g., lack of interpretability and too many false positives). KnowledgeInfused Learning (KIL) has the potential to address current limitations through different techniques. One possible approach relies on the adoption of Auxiliary Knowledge (AK), which uses domain knowledge to extract and engineer new features present in the raw data and provides additional context that helps the model better understand and differentiate between legitimate and malicious data. The main goal of this research is to propose a method that uses Auxiliary Knowledge (AK) to improve ML performance in detecting cyberattacks. We leveraged relevant domain knowledge to generate features from the raw data that are difficult for an ML model to discover. This approach also reduces the dependence on large amount of training data (big data) that is necessary for better ML predictions. The experiments used the CICIoT2023 dataset and demonstrated that auxiliary knowledge improves the detection performance, paving the way for future integration of automated knowledge management approaches. |
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| Date de publication | 2025-11-21 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 0461465c-14ac-4fc1-8c71-d8866f7e98db |
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| Enregistrement créé | 2026-03-26 |
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| Enregistrement modifié | 2026-05-11 |
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