Mitigating Out-of-Vocabulary Challenges in Embedded devices Vulnerability Classification: An Ensemble Embedding Approach with Bidirectional Context Modeling.
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| Title: | Mitigating Out-of-Vocabulary Challenges in Embedded devices Vulnerability Classification: An Ensemble Embedding Approach with Bidirectional Context Modeling. |
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| Authors: | Ben Yahya, Aissa1 ai@edu.umi.ac.ma, El Akhal, Hicham1 hi@edu.umi.ac.ma, El Belrhiti El Alaoui, Abdelbaki1 a.elbelrhitielalaoui@umi.ac.ma |
| Source: | Computer Science & Information Systems. Jan2026, Vol. 23 Issue 1, p397-417. 21p. |
| Subjects: | Embedded computer systems, Ensemble learning, Internet security, Infrastructure (Economics) |
| Abstract: | Critical infrastructure is increasingly reliant on embedded systems, which are particularly vulnerable to cyberattacks due to their inherent complexity and interconnectivity. Accurate classification of vulnerabilities in these systems is essential for targeted analysis and mitigation strategies. While pre-trained word embeddings such as Word2Vec, GloVe, and FastText are commonly used for this purpose, their effectiveness is limited by reliance on training corpora that lack domainspecific terminology, leading to challenges with Out-of-Vocabulary words and reduced classification performance. To address this limitation, we propose a novel ensemble embedding technique that combines multiple pre-trained embeddings to improve vulnerability classification in embedded systems. Evaluated on benchmark datasets, including the National Vulnerability Database and the China National Vulnerability Database, our method achieves a 91.50% F1-score on unseen data, outperforming traditional single-embedding approaches. This advancement demonstrates significant potential for enhancing cybersecurity in critical infrastructure applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192054652 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mitigating Out-of-Vocabulary Challenges in Embedded devices Vulnerability Classification: An Ensemble Embedding Approach with Bidirectional Context Modeling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ben+Yahya%2C+Aissa%22">Ben Yahya, Aissa</searchLink><relatesTo>1</relatesTo><i> ai@edu.umi.ac.ma</i><br /><searchLink fieldCode="AR" term="%22El+Akhal%2C+Hicham%22">El Akhal, Hicham</searchLink><relatesTo>1</relatesTo><i> hi@edu.umi.ac.ma</i><br /><searchLink fieldCode="AR" term="%22El+Belrhiti+El+Alaoui%2C+Abdelbaki%22">El Belrhiti El Alaoui, Abdelbaki</searchLink><relatesTo>1</relatesTo><i> a.elbelrhitielalaoui@umi.ac.ma</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Science+%26+Information+Systems%22">Computer Science & Information Systems</searchLink>. Jan2026, Vol. 23 Issue 1, p397-417. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Embedded+computer+systems%22">Embedded computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Infrastructure+%28Economics%29%22">Infrastructure (Economics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Critical infrastructure is increasingly reliant on embedded systems, which are particularly vulnerable to cyberattacks due to their inherent complexity and interconnectivity. Accurate classification of vulnerabilities in these systems is essential for targeted analysis and mitigation strategies. While pre-trained word embeddings such as Word2Vec, GloVe, and FastText are commonly used for this purpose, their effectiveness is limited by reliance on training corpora that lack domainspecific terminology, leading to challenges with Out-of-Vocabulary words and reduced classification performance. To address this limitation, we propose a novel ensemble embedding technique that combines multiple pre-trained embeddings to improve vulnerability classification in embedded systems. Evaluated on benchmark datasets, including the National Vulnerability Database and the China National Vulnerability Database, our method achieves a 91.50% F1-score on unseen data, outperforming traditional single-embedding approaches. This advancement demonstrates significant potential for enhancing cybersecurity in critical infrastructure applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2298/CSIS250314079B Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 397 Subjects: – SubjectFull: Embedded computer systems Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Internet security Type: general – SubjectFull: Infrastructure (Economics) Type: general Titles: – TitleFull: Mitigating Out-of-Vocabulary Challenges in Embedded devices Vulnerability Classification: An Ensemble Embedding Approach with Bidirectional Context Modeling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ben Yahya, Aissa – PersonEntity: Name: NameFull: El Akhal, Hicham – PersonEntity: Name: NameFull: El Belrhiti El Alaoui, Abdelbaki IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18200214 Numbering: – Type: volume Value: 23 – Type: issue Value: 1 Titles: – TitleFull: Computer Science & Information Systems Type: main |
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