Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron
With the proliferation of mobile devices, the popularity of Android applications (apps) has increased exponentially. Efficient power consumption in a device is essential from the perspective of the user because users want their devices to work all day. Developers must properly utilize the applicatio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/18/2211 |
| _version_ | 1797519553763213312 |
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| author | Muhammad Umair Khan Scott Uk-Jin Lee Zhiqiang Wu Shanza Abbas |
| author_facet | Muhammad Umair Khan Scott Uk-Jin Lee Zhiqiang Wu Shanza Abbas |
| author_sort | Muhammad Umair Khan |
| collection | DOAJ |
| description | With the proliferation of mobile devices, the popularity of Android applications (apps) has increased exponentially. Efficient power consumption in a device is essential from the perspective of the user because users want their devices to work all day. Developers must properly utilize the application programming interfaces (APIs) provided by Android software development kit to optimize the power consumption of their app. Occasionally, developers fail to relinquish the resources required by their app, resulting in a resource leak. Wake lock APIs are used in apps to manage the power state of the Android smartphone, and they frequently consume more power than necessary if not used appropriately (also called energy leak). In this study, we use a multi-layer perceptron (MLP) to detect wake lock leaks in Android apps because the MLP can solve complex problems and determine similarities in graphs. To detect wake lock leaks, we extract the call graph as features from the APK and embed the instruction and neighbor information in the node’s label of the call graph. Then, the encoded data are input to an MLP model for training and testing. We demonstrate that our model can identify wake lock leaks in apps with 99% accuracy. |
| first_indexed | 2024-03-10T07:44:24Z |
| format | Article |
| id | doaj.art-5de7dc24d5b143cba3b190a8a612415d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T07:44:24Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-5de7dc24d5b143cba3b190a8a612415d2023-11-22T12:47:31ZengMDPI AGElectronics2079-92922021-09-011018221110.3390/electronics10182211Wake Lock Leak Detection in Android Apps Using Multi-Layer PerceptronMuhammad Umair Khan0Scott Uk-Jin Lee1Zhiqiang Wu2Shanza Abbas3Department of Computer Science and Engineering, Hanyang University, Ansan 15588, KoreaDepartment of Computer Science and Engineering, Hanyang University, Ansan 15588, KoreaDepartment of Computer Science and Engineering, Hanyang University, Ansan 15588, KoreaDepartment of Computer Science and Engineering, Hanyang University, Ansan 15588, KoreaWith the proliferation of mobile devices, the popularity of Android applications (apps) has increased exponentially. Efficient power consumption in a device is essential from the perspective of the user because users want their devices to work all day. Developers must properly utilize the application programming interfaces (APIs) provided by Android software development kit to optimize the power consumption of their app. Occasionally, developers fail to relinquish the resources required by their app, resulting in a resource leak. Wake lock APIs are used in apps to manage the power state of the Android smartphone, and they frequently consume more power than necessary if not used appropriately (also called energy leak). In this study, we use a multi-layer perceptron (MLP) to detect wake lock leaks in Android apps because the MLP can solve complex problems and determine similarities in graphs. To detect wake lock leaks, we extract the call graph as features from the APK and embed the instruction and neighbor information in the node’s label of the call graph. Then, the encoded data are input to an MLP model for training and testing. We demonstrate that our model can identify wake lock leaks in apps with 99% accuracy.https://www.mdpi.com/2079-9292/10/18/2211wake lockAndroidoversamplingpower consumptionmulti-layer perceptron |
| spellingShingle | Muhammad Umair Khan Scott Uk-Jin Lee Zhiqiang Wu Shanza Abbas Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron Electronics wake lock Android oversampling power consumption multi-layer perceptron |
| title | Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron |
| title_full | Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron |
| title_fullStr | Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron |
| title_full_unstemmed | Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron |
| title_short | Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron |
| title_sort | wake lock leak detection in android apps using multi layer perceptron |
| topic | wake lock Android oversampling power consumption multi-layer perceptron |
| url | https://www.mdpi.com/2079-9292/10/18/2211 |
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