Academic Journal
Malware Classification Technology Combining Multimodal Fusion with Deep Learning Algorithms.
| Τίτλος: | Malware Classification Technology Combining Multimodal Fusion with Deep Learning Algorithms. |
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| Συγγραφείς: | Shuiping Wang, Yanzhen Wang |
| Πηγή: | Journal of Cyber Security & Mobility; 2025, Vol. 14 Issue 3, p597-621, 25p |
| Θεματικοί όροι: | Malware, Computer network security, Bidirectional associative memories (Computer science), Artificial neural networks, Deep learning, Application program interfaces |
| Περίληψη: | This paper proposes a malware family classification framework based on multimodal fusion to improve the accuracy of malware classification and create a reliable network security environment. In addition, this paper uses a method that combines Bi-LSTM (Bidirectional Long Short-Term Memory) and 1D-CNN (One-Dimensional Convolutional Neural Network) to fully mine the contextual semantic information of API (Application Programming Interface) call sequences, generates initial prototypes for each family through the prototype network, and dynamically generates multiple prototypes for the family through multiple iterations. In addition, this paper adjusts and allocates multiple prototypes of the family based on the probability calculation method of the Gaussian mixture model, and uses it as the final family classifier. Finally, this paper verifies the model effect through experiments. The experimental results show that the family classification accuracy of Bi-LSTM-1D-CNN can reach more than 80%, which is better than the classification accuracy of other methods. At the same time, compared with the infinite hybrid prototype network IMP (Infinite Mixture Prototypes Network), the method proposed in this paper integrates the supervision information in the support set label into the decision-making of prototype establishment, so that it can better participate in the model training process. Furthermore, through the powerful massive data analysis and computing capabilities of deep learning technology, it is possible to effectively realize the automatic detection and classification of malware, and to build a classification model by mining the multiple modes of malware, so as to better play the application advantages of machine learning technology in this field. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Cyber Security & Mobility is the property of River Publishers 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 187225985 RelevancyScore: 994 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 993.548583984375 |
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| Items | – Name: Title Label: Title Group: Ti Data: Malware Classification Technology Combining Multimodal Fusion with Deep Learning Algorithms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shuiping+Wang%22">Shuiping Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Yanzhen+Wang%22">Yanzhen Wang</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Cyber Security & Mobility; 2025, Vol. 14 Issue 3, p597-621, 25p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Malware%22">Malware</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+security%22">Computer network security</searchLink><br /><searchLink fieldCode="DE" term="%22Bidirectional+associative+memories+%28Computer+science%29%22">Bidirectional associative memories (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Application+program+interfaces%22">Application program interfaces</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes a malware family classification framework based on multimodal fusion to improve the accuracy of malware classification and create a reliable network security environment. In addition, this paper uses a method that combines Bi-LSTM (Bidirectional Long Short-Term Memory) and 1D-CNN (One-Dimensional Convolutional Neural Network) to fully mine the contextual semantic information of API (Application Programming Interface) call sequences, generates initial prototypes for each family through the prototype network, and dynamically generates multiple prototypes for the family through multiple iterations. In addition, this paper adjusts and allocates multiple prototypes of the family based on the probability calculation method of the Gaussian mixture model, and uses it as the final family classifier. Finally, this paper verifies the model effect through experiments. The experimental results show that the family classification accuracy of Bi-LSTM-1D-CNN can reach more than 80%, which is better than the classification accuracy of other methods. At the same time, compared with the infinite hybrid prototype network IMP (Infinite Mixture Prototypes Network), the method proposed in this paper integrates the supervision information in the support set label into the decision-making of prototype establishment, so that it can better participate in the model training process. Furthermore, through the powerful massive data analysis and computing capabilities of deep learning technology, it is possible to effectively realize the automatic detection and classification of malware, and to build a classification model by mining the multiple modes of malware, so as to better play the application advantages of machine learning technology in this field. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Cyber Security & Mobility is the property of River Publishers 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: 10.13052/jcsm2245-1439.1434 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 597 Subjects: – SubjectFull: Malware Type: general – SubjectFull: Computer network security Type: general – SubjectFull: Bidirectional associative memories (Computer science) Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Application program interfaces Type: general Titles: – TitleFull: Malware Classification Technology Combining Multimodal Fusion with Deep Learning Algorithms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shuiping Wang – PersonEntity: Name: NameFull: Yanzhen Wang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 22451439 Numbering: – Type: volume Value: 14 – Type: issue Value: 3 Titles: – TitleFull: Journal of Cyber Security & Mobility Type: main |
| ResultId | 1 |