Cited By
View all- Bhat PBehal SDutta K(2023)A system call-based android malware detection approach with homogeneous & heterogeneous ensemble machine learningComputers and Security10.1016/j.cose.2023.103277130:COnline publication date: 1-Jul-2023
ADAM: Automatic Detection of Android Malware
Pages 18 - 31
Abstract
The popularity of the Android operating system has been rising ever since its initial release in 2008. This is due to two major reasons. The first is that Android is open-source, due to which a lot of mobile manufacturing companies use some form of modified Android OS for their devices. The second reason is that a wide variety of applications with different designs and utility can be built with ease for Android devices. With this much popularity, gaining unwanted attention of cybercriminals is inevitable. Hence, there has been a huge rise in the number of malware being developed for Android devices. To address this problem, we present ADAM (Automatic Detection of Android Malware), an Android application that uses machine learning (ML) for automatic detection of malware in Android applications. ADAM is trained with CICMalDroid 2020 Android Malware dataset and tested for both CICMalDroid 2020 and CICMalDroid 2017 dataset. The experiment analysis showed that it achieves more than 98.5% accuracy. ADAM considers only static analysis, so becomes easy to deploy in smart phone to alert the user. ADAM is deployed over android mobile phone.
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Index Terms
- ADAM: Automatic Detection of Android Malware
Index terms have been assigned to the content through auto-classification.
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Nov 2021
344 pages
© Springer Nature Switzerland AG 2022.
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Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 25 November 2021
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View all- Bhat PBehal SDutta K(2023)A system call-based android malware detection approach with homogeneous & heterogeneous ensemble machine learningComputers and Security10.1016/j.cose.2023.103277130:COnline publication date: 1-Jul-2023