A multi-model ensemble learning framework for imbalanced android malware detection.
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| Title: | A multi-model ensemble learning framework for imbalanced android malware detection. |
|---|---|
| Authors: | Zhu, Hui-juan1 (AUTHOR) huijuanzhu@ujs.edu.cn, Li, Yang1 (AUTHOR) lyml6@foxmail.com, Wang, Liang-min1,2 (AUTHOR) liangmin@seu.edu.cn, Sheng, Victor S.1,3 (AUTHOR) victor.sheng@ttu.edu |
| Source: | Expert Systems with Applications. Dec2023, Vol. 234, pN.PAG-N.PAG. 1p. |
| Subjects: | Machine learning, Feature extraction, Malware, Deep learning, Algorithms |
| Abstract: | The continuous malicious software (malware) attacks on smartphones pose a serious threat to the security of users, especially the dominant platform Android. Data-driven methods based on machine learning algorithms are a promising way to defend against that. In this paper, we explore the limitations of this kind of methods in improving the performance of malware detection, e.g., considering each feature in isolation and relying on balanced class data, and propose a multi-model ensemble framework MEFDroid by combining individual predictors, where hybrid deep learning based feature extraction methods are adopted to learn meaningful features from raw data. Besides, a novel fusion scheme is exploited to fuse multi-level representations and mine their correlations to maximize the utilization of original information. To evaluate the effectiveness of the MEFDroid framework, we conduct a step-by-step model justification experiment to investigate how the proposed algorithms (i.e., ESAES, EDAES and EDAFS) enhance the malware detection performance gradually. We also compare the proposed algorithms with classical machine learning methods, conventional sampling solutions for the imbalance problem. Besides, we investigate the reliability of our proposed methods using another public dataset. Our extensive experimental results demonstrate that the target model EDAFS in MEFDroid outperforms others in terms of most metrics, which means that it is an effective alternative solution to detect Android malware. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 172776968 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A multi-model ensemble learning framework for imbalanced android malware detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhu%2C+Hui-juan%22">Zhu, Hui-juan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> huijuanzhu@ujs.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Yang%22">Li, Yang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lyml6@foxmail.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Liang-min%22">Wang, Liang-min</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> liangmin@seu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Sheng%2C+Victor+S%2E%22">Sheng, Victor S.</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> victor.sheng@ttu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Dec2023, Vol. 234, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Malware%22">Malware</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The continuous malicious software (malware) attacks on smartphones pose a serious threat to the security of users, especially the dominant platform Android. Data-driven methods based on machine learning algorithms are a promising way to defend against that. In this paper, we explore the limitations of this kind of methods in improving the performance of malware detection, e.g., considering each feature in isolation and relying on balanced class data, and propose a multi-model ensemble framework MEFDroid by combining individual predictors, where hybrid deep learning based feature extraction methods are adopted to learn meaningful features from raw data. Besides, a novel fusion scheme is exploited to fuse multi-level representations and mine their correlations to maximize the utilization of original information. To evaluate the effectiveness of the MEFDroid framework, we conduct a step-by-step model justification experiment to investigate how the proposed algorithms (i.e., ESAES, EDAES and EDAFS) enhance the malware detection performance gradually. We also compare the proposed algorithms with classical machine learning methods, conventional sampling solutions for the imbalance problem. Besides, we investigate the reliability of our proposed methods using another public dataset. Our extensive experimental results demonstrate that the target model EDAFS in MEFDroid outperforms others in terms of most metrics, which means that it is an effective alternative solution to detect Android malware. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science 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: 10.1016/j.eswa.2023.120952 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Malware Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: A multi-model ensemble learning framework for imbalanced android malware detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhu, Hui-juan – PersonEntity: Name: NameFull: Li, Yang – PersonEntity: Name: NameFull: Wang, Liang-min – PersonEntity: Name: NameFull: Sheng, Victor S. IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 12 Text: Dec2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09574174 Numbering: – Type: volume Value: 234 Titles: – TitleFull: Expert Systems with Applications Type: main |
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