HamDroid: permission-based harmful android anti-malware detection using neural networks.
Saved in:
| Title: | HamDroid: permission-based harmful android anti-malware detection using neural networks. |
|---|---|
| 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| 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] |
|---|---|
| ISSN: | 09410643 |
| DOI: | 10.1007/s00521-021-06755-4 |
