HamDroid: permission-based harmful android anti-malware detection using neural networks.
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| Title: | HamDroid: permission-based harmful android anti-malware detection using neural networks. |
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| Authors: | Seraj, Saeed1 (AUTHOR), Khodambashi, Siavash2 (AUTHOR), Pavlidis, Michalis1 (AUTHOR), Polatidis, Nikolaos1 (AUTHOR) N.Polatidis@Brighton.ac.uk |
| Source: | Neural Computing & Applications. Sep2022, Vol. 34 Issue 18, p15165-15174. 10p. |
| Subjects: | Anti-malware (Computer software), Computer software industry, Multilayer perceptrons |
| Abstract: | Android platforms are a popular target for attackers, while many users around the world are victims of Android malwares threatening their private information. Numerous Android anti-malware applications are fake and do not work as advertised because they have been developed either by amateur programmers or by software companies that are not focused on the security aspects of the business. Such applications usually ask for and generally receive non-necessary permissions which at the end collect sensitive information. The rapidly developing fake anti-malware is a serious problem, and there is a need for detection of harmful Android anti-malware. This article delivers a dataset of Android anti-malware, including malicious or benign, and a customized multilayer perceptron neural network that is being used to detect anti-malware based on the permissions of the applications. The results show that the proposed method can detect with very high accuracy fake anti-malware, while it outperforms other standard classifiers in terms of accuracy, precision, and recall. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 158693822 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: HamDroid: permission-based harmful android anti-malware detection using neural networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Seraj%2C+Saeed%22">Seraj, Saeed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Khodambashi%2C+Siavash%22">Khodambashi, Siavash</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pavlidis%2C+Michalis%22">Pavlidis, Michalis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Polatidis%2C+Nikolaos%22">Polatidis, Nikolaos</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> N.Polatidis@Brighton.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Sep2022, Vol. 34 Issue 18, p15165-15174. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Anti-malware+%28Computer+software%29%22">Anti-malware (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+industry%22">Computer software industry</searchLink><br /><searchLink fieldCode="DE" term="%22Multilayer+perceptrons%22">Multilayer perceptrons</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Android platforms are a popular target for attackers, while many users around the world are victims of Android malwares threatening their private information. Numerous Android anti-malware applications are fake and do not work as advertised because they have been developed either by amateur programmers or by software companies that are not focused on the security aspects of the business. Such applications usually ask for and generally receive non-necessary permissions which at the end collect sensitive information. The rapidly developing fake anti-malware is a serious problem, and there is a need for detection of harmful Android anti-malware. This article delivers a dataset of Android anti-malware, including malicious or benign, and a customized multilayer perceptron neural network that is being used to detect anti-malware based on the permissions of the applications. The results show that the proposed method can detect with very high accuracy fake anti-malware, while it outperforms other standard classifiers in terms of accuracy, precision, and recall. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=158693822 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-021-06755-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 15165 Subjects: – SubjectFull: Anti-malware (Computer software) Type: general – SubjectFull: Computer software industry Type: general – SubjectFull: Multilayer perceptrons Type: general Titles: – TitleFull: HamDroid: permission-based harmful android anti-malware detection using neural networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Seraj, Saeed – PersonEntity: Name: NameFull: Khodambashi, Siavash – PersonEntity: Name: NameFull: Pavlidis, Michalis – PersonEntity: Name: NameFull: Polatidis, Nikolaos IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 34 – Type: issue Value: 18 Titles: – TitleFull: Neural Computing & Applications Type: main |
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