Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics
Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, securit...
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| Format: | Article |
| Language: | English English |
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Springer US
2017
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| Online Access: | http://umpir.ump.edu.my/id/eprint/17481/ http://umpir.ump.edu.my/id/eprint/17481/1/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics.pdf http://umpir.ump.edu.my/id/eprint/17481/12/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics%201.pdf |
| _version_ | 1848820440310480896 |
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| author | Ahmad, Firdaus Nor Badrul, Anuar Mohd Faizal, Ab Razak Sangaiah, Arun Kumar |
| author_facet | Ahmad, Firdaus Nor Badrul, Anuar Mohd Faizal, Ab Razak Sangaiah, Arun Kumar |
| author_sort | Ahmad, Firdaus |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, directory path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system. |
| first_indexed | 2025-11-15T02:09:29Z |
| format | Article |
| id | ump-17481 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:09:29Z |
| publishDate | 2017 |
| publisher | Springer US |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-174812017-07-21T01:35:34Z http://umpir.ump.edu.my/id/eprint/17481/ Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics Ahmad, Firdaus Nor Badrul, Anuar Mohd Faizal, Ab Razak Sangaiah, Arun Kumar QA76 Computer software Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, directory path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system. Springer US 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17481/1/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/17481/12/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics%201.pdf Ahmad, Firdaus and Nor Badrul, Anuar and Mohd Faizal, Ab Razak and Sangaiah, Arun Kumar (2017) Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics. Multimedia Tools and Applications. pp. 1-37. ISSN 1380-7501(print); 1573-7721(online). (Published) https://doi.org/10.1007/s11042-017-4586-0 DOI: 10.1007/s11042-017-4586-0 |
| spellingShingle | QA76 Computer software Ahmad, Firdaus Nor Badrul, Anuar Mohd Faizal, Ab Razak Sangaiah, Arun Kumar Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics |
| title | Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics |
| title_full | Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics |
| title_fullStr | Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics |
| title_full_unstemmed | Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics |
| title_short | Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics |
| title_sort | bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/17481/ http://umpir.ump.edu.my/id/eprint/17481/ http://umpir.ump.edu.my/id/eprint/17481/ http://umpir.ump.edu.my/id/eprint/17481/1/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics.pdf http://umpir.ump.edu.my/id/eprint/17481/12/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics%201.pdf |