Multimodal information fusion for android malware detection using lazy learning
Android has a large number of users that are accumulating with each passing day. Security of the Android ecosystem is a major concern for these users with the provision of quality services. In this paper, multimodal analysis of malware apps has been presented. We exploit static, dynamic, and visual...
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Published in | Multimedia tools and applications Vol. 81; no. 9; pp. 12077 - 12091 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.04.2022
Springer Nature B.V |
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Abstract | Android has a large number of users that are accumulating with each passing day. Security of the Android ecosystem is a major concern for these users with the provision of quality services. In this paper, multimodal analysis of malware apps has been presented. We exploit static, dynamic, and visual features of apps to predict the malicious apps using information fusion. The proposed study applies case-based reasoning; for catalyzing the process of training and validation over renowned datasets with enriched feature-set. Our proposed semi-supervised technique uses benign and malicious apps to predict and classify malware. The prediction process uses a hybrid analysis of malware. The proposed approach, due to the efficient and adaptive nature of CBR, outperforms prevalent approaches. Our approach has an accuracy of 95% and reduced rate of false negative rate and a better precision metric, which beat the state-of-the-art techniques. |
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AbstractList | Android has a large number of users that are accumulating with each passing day. Security of the Android ecosystem is a major concern for these users with the provision of quality services. In this paper, multimodal analysis of malware apps has been presented. We exploit static, dynamic, and visual features of apps to predict the malicious apps using information fusion. The proposed study applies case-based reasoning; for catalyzing the process of training and validation over renowned datasets with enriched feature-set. Our proposed semi-supervised technique uses benign and malicious apps to predict and classify malware. The prediction process uses a hybrid analysis of malware. The proposed approach, due to the efficient and adaptive nature of CBR, outperforms prevalent approaches. Our approach has an accuracy of 95% and reduced rate of false negative rate and a better precision metric, which beat the state-of-the-art techniques. |
Author | Qaisar, Zahid Hussain Li, Ruixuan |
Author_xml | – sequence: 1 givenname: Zahid Hussain surname: Qaisar fullname: Qaisar, Zahid Hussain email: zahidqaisar@hust.edu.cn organization: School of Computer Science and Technology, Huazhong University of Science and Technology – sequence: 2 givenname: Ruixuan surname: Li fullname: Li, Ruixuan organization: School of Computer Science and Technology, Huazhong University of Science and Technology |
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CitedBy_id | crossref_primary_10_1002_ail2_94 crossref_primary_10_1111_exsy_13482 crossref_primary_10_3390_electronics10131606 crossref_primary_10_3390_s22062268 crossref_primary_10_1016_j_eswa_2023_121155 crossref_primary_10_3233_JIFS_231969 crossref_primary_10_1007_s12008_023_01578_0 crossref_primary_10_32604_cmc_2022_030878 |
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Keywords | Android security Lazy learning Malware detection Multimodal fusion Case-based reasoning |
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SubjectTerms | 1177: Advances in Deep Learning for Multimodal Fusion and Alignment Computer Communication Networks Computer Science Data integration Data Structures and Information Theory Malware Multimedia Information Systems Special Purpose and Application-Based Systems |
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Title | Multimodal information fusion for android malware detection using lazy learning |
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