MiniMon: Minimizing Android Applications with Intelligent Monitoring-Based Debloating.
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| Title: | MiniMon: Minimizing Android Applications with Intelligent Monitoring-Based Debloating. |
|---|---|
| Authors: | Liu, Jiakun1 jkliu@smu.edu.sg, Zhang, Zicheng1 zczhang.2020@phdcs.smu.edu.sg, Hu, Xing2 xinghu@zju.edu.cn, Thung, Ferdian1 ferdianthung@smu.edu.sg, Maoz, Shahar3 maoz@cs.tau.ac.il, Gao, Debin1 dbgao@smu.edu.sg, Toch, Eran3 erant@tauex.tau.ac.il, Zhao, Zhipeng1 zpzhao@smu.edu.sg, Lo, David1 davidlo@smu.edu.sg |
| Source: | ICSE: International Conference on Software Engineering. 2024, p1-13. 13p. |
| Subjects: | Android (Operating system), Computer users, Database management, Logarithms, Algebra |
| Abstract: | The size of Android applications is getting larger to fulfill the requirements of various users. However, not all the features of the applications are needed and desired by a specific user. The unnecessary and non-desired features can increase the attack surface and consume system resources such as storage and memory. To address this issue, we propose a framework, MiniMon, to debloat unnecessary features from an Android app based on the logs of specific users' interactions with the app. However, rarely used features may not be recorded during the data collection, and users' preferences may change slightly over time. To address these challenges, we embed several solutions in our framework that can uncover user-desired features by learning and generalizing from the logs of how users interact with an application. MiniMon first collects the application methods that are executed when users interact with it. Then, given the collected executed methods and the call graph of the application, MiniMon applies 10 techniques to generalize from logs. These include three program analysis-based techniques, two graph clustering-based techniques, and five graph embedding-based techniques to identify the additional methods in an app that are similar to the logged executed methods. Finally, MiniMon generates a debloated application by removing methods that are not similar to the executed methods. To evaluate the performance of variants of MiniMon that use different generalization techniques, we create a benchmark for a controlled experiment. The results show that the graph embedding-based generalization technique that considers the information of all nodes in the call graph is the best, and can correctly uncover 75.5% of the unobserved but desired behaviors and still debloat more than half of the app. We also conducted a user study that uncovers that the use of the intelligent (generalization) method of MiniMon boosts the overall user satisfaction rate by 37.6%. [ABSTRACT FROM AUTHOR] |
| Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery 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: 185196393 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: MiniMon: Minimizing Android Applications with Intelligent Monitoring-Based Debloating. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Jiakun%22">Liu, Jiakun</searchLink><relatesTo>1</relatesTo><i> jkliu@smu.edu.sg</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zicheng%22">Zhang, Zicheng</searchLink><relatesTo>1</relatesTo><i> zczhang.2020@phdcs.smu.edu.sg</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Xing%22">Hu, Xing</searchLink><relatesTo>2</relatesTo><i> xinghu@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Thung%2C+Ferdian%22">Thung, Ferdian</searchLink><relatesTo>1</relatesTo><i> ferdianthung@smu.edu.sg</i><br /><searchLink fieldCode="AR" term="%22Maoz%2C+Shahar%22">Maoz, Shahar</searchLink><relatesTo>3</relatesTo><i> maoz@cs.tau.ac.il</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Debin%22">Gao, Debin</searchLink><relatesTo>1</relatesTo><i> dbgao@smu.edu.sg</i><br /><searchLink fieldCode="AR" term="%22Toch%2C+Eran%22">Toch, Eran</searchLink><relatesTo>3</relatesTo><i> erant@tauex.tau.ac.il</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Zhipeng%22">Zhao, Zhipeng</searchLink><relatesTo>1</relatesTo><i> zpzhao@smu.edu.sg</i><br /><searchLink fieldCode="AR" term="%22Lo%2C+David%22">Lo, David</searchLink><relatesTo>1</relatesTo><i> davidlo@smu.edu.sg</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 2024, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Android+%28Operating+system%29%22">Android (Operating system)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+users%22">Computer users</searchLink><br /><searchLink fieldCode="DE" term="%22Database+management%22">Database management</searchLink><br /><searchLink fieldCode="DE" term="%22Logarithms%22">Logarithms</searchLink><br /><searchLink fieldCode="DE" term="%22Algebra%22">Algebra</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The size of Android applications is getting larger to fulfill the requirements of various users. However, not all the features of the applications are needed and desired by a specific user. The unnecessary and non-desired features can increase the attack surface and consume system resources such as storage and memory. To address this issue, we propose a framework, MiniMon, to debloat unnecessary features from an Android app based on the logs of specific users' interactions with the app. However, rarely used features may not be recorded during the data collection, and users' preferences may change slightly over time. To address these challenges, we embed several solutions in our framework that can uncover user-desired features by learning and generalizing from the logs of how users interact with an application. MiniMon first collects the application methods that are executed when users interact with it. Then, given the collected executed methods and the call graph of the application, MiniMon applies 10 techniques to generalize from logs. These include three program analysis-based techniques, two graph clustering-based techniques, and five graph embedding-based techniques to identify the additional methods in an app that are similar to the logged executed methods. Finally, MiniMon generates a debloated application by removing methods that are not similar to the executed methods. To evaluate the performance of variants of MiniMon that use different generalization techniques, we create a benchmark for a controlled experiment. The results show that the graph embedding-based generalization technique that considers the information of all nodes in the call graph is the best, and can correctly uncover 75.5% of the unobserved but desired behaviors and still debloat more than half of the app. We also conducted a user study that uncovers that the use of the intelligent (generalization) method of MiniMon boosts the overall user satisfaction rate by 37.6%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1145/3597503.3639113 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Android (Operating system) Type: general – SubjectFull: Computer users Type: general – SubjectFull: Database management Type: general – SubjectFull: Logarithms Type: general – SubjectFull: Algebra Type: general Titles: – TitleFull: MiniMon: Minimizing Android Applications with Intelligent Monitoring-Based Debloating. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Jiakun – PersonEntity: Name: NameFull: Zhang, Zicheng – PersonEntity: Name: NameFull: Hu, Xing – PersonEntity: Name: NameFull: Thung, Ferdian – PersonEntity: Name: NameFull: Maoz, Shahar – PersonEntity: Name: NameFull: Gao, Debin – PersonEntity: Name: NameFull: Toch, Eran – PersonEntity: Name: NameFull: Zhao, Zhipeng – PersonEntity: Name: NameFull: Lo, David IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2024 Type: published Y: 2024 Titles: – TitleFull: ICSE: International Conference on Software Engineering Type: main |
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