SeqAdver: Automatic payload construction and injection in sequence-based Android adversarial attack

Machine learning has achieved a great success in the field of Android malware detection. In order to avoid being caught by these ML-based Android malware detection, malware authors are inclined to initiate adversarial sample attacks by tampering with mobile applications. Although machine learning ha...

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Main Authors: ZHANG, Fei, FENG, Ruitao, XIE, Xiaofei, LI, Xiaohong, SHI, Lianshuan
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/sis_research/8707
https://ink.library.smu.edu.sg/context/sis_research/article/9710/viewcontent/SeqAdver_av.pdf
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spelling sg-smu-ink.sis_research-97102024-04-04T09:08:10Z SeqAdver: Automatic payload construction and injection in sequence-based Android adversarial attack ZHANG, Fei FENG, Ruitao XIE, Xiaofei LI, Xiaohong SHI, Lianshuan Machine learning has achieved a great success in the field of Android malware detection. In order to avoid being caught by these ML-based Android malware detection, malware authors are inclined to initiate adversarial sample attacks by tampering with mobile applications. Although machine learning has high capability, it lacks robustness against adversarial attacks. Currently, many of the adversarial attacking tools not only inject dead code into target applications, which can never be executed, but also require the injection of many benign features into a malicious APK. This can be easily noticeable by program analysis techniques. In this paper, we propose SeqAdver, an automatic payload construction and injection tool, which aims to bring the adversarial attack to the next level by injecting a payload that allows execution without breaking the app's original functionalities. These payloads are obtained from benign APKs at the Smali level and normalized into usable code snippets. The extracted Smali codes are carefully selected by filtering out 'user-visible' APIs and Intents. Therefore, payloads are able to be executed without any visible change noticed by the user. Besides, extracted payloads can be injected into different locations of the file based on sequence position or on the launcher class. Experiments were conducted to prove that randomly extracted payloads from benign apps are able to execute without causing any 'user-visible' behaviors or crashing the app when running the app in Android emulators. 2023-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8707 info:doi/10.1109/ICDMW60847.2023.00172 https://ink.library.smu.edu.sg/context/sis_research/article/9710/viewcontent/SeqAdver_av.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 Adversarial attack Android malware Payload injection Information Security
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Adversarial attack
Android malware
Payload injection
Information Security
spellingShingle Adversarial attack
Android malware
Payload injection
Information Security
ZHANG, Fei
FENG, Ruitao
XIE, Xiaofei
LI, Xiaohong
SHI, Lianshuan
SeqAdver: Automatic payload construction and injection in sequence-based Android adversarial attack
description Machine learning has achieved a great success in the field of Android malware detection. In order to avoid being caught by these ML-based Android malware detection, malware authors are inclined to initiate adversarial sample attacks by tampering with mobile applications. Although machine learning has high capability, it lacks robustness against adversarial attacks. Currently, many of the adversarial attacking tools not only inject dead code into target applications, which can never be executed, but also require the injection of many benign features into a malicious APK. This can be easily noticeable by program analysis techniques. In this paper, we propose SeqAdver, an automatic payload construction and injection tool, which aims to bring the adversarial attack to the next level by injecting a payload that allows execution without breaking the app's original functionalities. These payloads are obtained from benign APKs at the Smali level and normalized into usable code snippets. The extracted Smali codes are carefully selected by filtering out 'user-visible' APIs and Intents. Therefore, payloads are able to be executed without any visible change noticed by the user. Besides, extracted payloads can be injected into different locations of the file based on sequence position or on the launcher class. Experiments were conducted to prove that randomly extracted payloads from benign apps are able to execute without causing any 'user-visible' behaviors or crashing the app when running the app in Android emulators.
format text
author ZHANG, Fei
FENG, Ruitao
XIE, Xiaofei
LI, Xiaohong
SHI, Lianshuan
author_facet ZHANG, Fei
FENG, Ruitao
XIE, Xiaofei
LI, Xiaohong
SHI, Lianshuan
author_sort ZHANG, Fei
title SeqAdver: Automatic payload construction and injection in sequence-based Android adversarial attack
title_short SeqAdver: Automatic payload construction and injection in sequence-based Android adversarial attack
title_full SeqAdver: Automatic payload construction and injection in sequence-based Android adversarial attack
title_fullStr SeqAdver: Automatic payload construction and injection in sequence-based Android adversarial attack
title_full_unstemmed SeqAdver: Automatic payload construction and injection in sequence-based Android adversarial attack
title_sort seqadver: automatic payload construction and injection in sequence-based android adversarial attack
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/sis_research/8707
https://ink.library.smu.edu.sg/context/sis_research/article/9710/viewcontent/SeqAdver_av.pdf
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