MiniMon: Minimizing Android applications with intelligent monitoring-based debloating

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...

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Main Authors: LIU, Jiakun, ZHANG, Zicheng, HU, Xing, Ferdian, Thung, MAOZ, Shahar, GAO, Debin, TOCH, Eran, ZHAO, Zhipeng, David LO
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9233
https://ink.library.smu.edu.sg/context/sis_research/article/10233/viewcontent/3597503.3639113.pdf
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spelling sg-smu-ink.sis_research-102332024-09-02T06:50:46Z MiniMon: Minimizing Android applications with intelligent monitoring-based debloating LIU, Jiakun ZHANG, Zicheng HU, Xing Ferdian, Thung MAOZ, Shahar GAO, Debin TOCH, Eran ZHAO, Zhipeng David LO, 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%. 2024-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9233 info:doi/10.1145/3597503.3639113 https://ink.library.smu.edu.sg/context/sis_research/article/10233/viewcontent/3597503.3639113.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Android Software Debloating Log Analysis Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Android
Software Debloating
Log Analysis
Software Engineering
spellingShingle Android
Software Debloating
Log Analysis
Software Engineering
LIU, Jiakun
ZHANG, Zicheng
HU, Xing
Ferdian, Thung
MAOZ, Shahar
GAO, Debin
TOCH, Eran
ZHAO, Zhipeng
David LO,
MiniMon: Minimizing Android applications with intelligent monitoring-based debloating
description 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%.
format text
author LIU, Jiakun
ZHANG, Zicheng
HU, Xing
Ferdian, Thung
MAOZ, Shahar
GAO, Debin
TOCH, Eran
ZHAO, Zhipeng
David LO,
author_facet LIU, Jiakun
ZHANG, Zicheng
HU, Xing
Ferdian, Thung
MAOZ, Shahar
GAO, Debin
TOCH, Eran
ZHAO, Zhipeng
David LO,
author_sort LIU, Jiakun
title MiniMon: Minimizing Android applications with intelligent monitoring-based debloating
title_short MiniMon: Minimizing Android applications with intelligent monitoring-based debloating
title_full MiniMon: Minimizing Android applications with intelligent monitoring-based debloating
title_fullStr MiniMon: Minimizing Android applications with intelligent monitoring-based debloating
title_full_unstemmed MiniMon: Minimizing Android applications with intelligent monitoring-based debloating
title_sort minimon: minimizing android applications with intelligent monitoring-based debloating
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9233
https://ink.library.smu.edu.sg/context/sis_research/article/10233/viewcontent/3597503.3639113.pdf
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