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Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps

Authors:

Carl A. GunterAuthors Info & Claims

ACSAC '17: Proceedings of the 33rd Annual Computer Security Applications Conference

December 2017

Pages 288 - 302

https://doi.org/10.1145/3134600.3134642

Published: 04 December 2017 Publication History

Abstract

Existing techniques on adversarial malware generation employ feature mutations based on feature vectors extracted from malware. However, most (if not all) of these techniques suffer from a common limitation: feasibility of these attacks is unknown. The synthesized mutations may break the inherent constraints posed by code structures of the malware, causing either crashes or malfunctioning of malicious payloads. To address the limitation, we present Malware Recomposition Variation (MRV), an approach that conducts semantic analysis of existing malware to systematically construct new malware variants for malware detectors to test and strengthen their detection signatures/models. In particular, we use two variation strategies (i.e., malware evolution attack and malware confusion attack) following structures of existing malware to enhance feasibility of the attacks. Upon the given malware, we conduct semantic-feature mutation analysis and phylogenetic analysis to synthesize mutation strategies. Based on these strategies, we perform program transplantation to automatically mutate malware bytecode to generate new malware variants. We evaluate our MRV approach on actual malware variants, and our empirical evaluation on 1,935 Android benign apps and 1,917 malware shows that MRV produces malware variants that can have high likelihood to evade detection while still retaining their malicious behaviors. We also propose and evaluate three defense mechanisms to counter MRV.

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Cited By

Zhou YCheng GYu SChen ZHu Y(2024)MTDroid: A Moving Target Defense-Based Android Malware Detector Against Evasion AttacksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.341433919(6377-6392)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3414339
Mekdad YNaseem FAris AOz HAcar ABabun LUluagac STuncay GGhani N(2024)On the Robustness of Image-Based Malware Detection Against Adversarial AttacksNetwork Security Empowered by Artificial Intelligence10.1007/978-3-031-53510-9_13(355-375)Online publication date: 24-Feb-2024
https://doi.org/10.1007/978-3-031-53510-9_13
Wang JWang LDong FWang HMontpetit MLeivadeas AUhlig SJaved M(2023)Re-measuring the Label Dynamics of Online Anti-Malware Engines from Millions of SamplesProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624800(253-267)Online publication date: 24-Oct-2023
https://dl.acm.org/doi/10.1145/3618257.3624800
Show More Cited By

Index Terms

Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation

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cover image ACM Other conferences

ACSAC '17: Proceedings of the 33rd Annual Computer Security Applications Conference

December 2017

618 pages

ISBN:9781450353458

DOI:10.1145/3134600

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 04 December 2017

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National Science Foundation

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ACSAC 2017

ACSAC 2017: 2017 Annual Computer Security Applications Conference

December 4 - 8, 2017

FL, Orlando, USA

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Overall Acceptance Rate 104 of 497 submissions, 21%

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Cited By

Zhou YCheng GYu SChen ZHu Y(2024)MTDroid: A Moving Target Defense-Based Android Malware Detector Against Evasion AttacksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.341433919(6377-6392)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3414339
Mekdad YNaseem FAris AOz HAcar ABabun LUluagac STuncay GGhani N(2024)On the Robustness of Image-Based Malware Detection Against Adversarial AttacksNetwork Security Empowered by Artificial Intelligence10.1007/978-3-031-53510-9_13(355-375)Online publication date: 24-Feb-2024
https://doi.org/10.1007/978-3-031-53510-9_13
Wang JWang LDong FWang HMontpetit MLeivadeas AUhlig SJaved M(2023)Re-measuring the Label Dynamics of Online Anti-Malware Engines from Millions of SamplesProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624800(253-267)Online publication date: 24-Oct-2023
https://dl.acm.org/doi/10.1145/3618257.3624800
Kollnig KDatta SSerban Von Davier TVan Kleek MBinns RLyngs UShadbolt N(2023)‘We are adults and deserve control of our phones’: Examining the risks and opportunities of a right to repair for mobile appsProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3593973(22-34)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3593973
Pimenta TCeschin FGregio A(2023)ANDROIDGYNY: Reviewing clustering techniques for Android malware family classificationDigital Threats: Research and Practice10.1145/3587471Online publication date: 14-Mar-2023
https://dl.acm.org/doi/10.1145/3587471
Rashid ASuch J(2023)MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-Based Malware DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.329395918(4361-4376)Online publication date: 2023
https://doi.org/10.1109/TIFS.2023.3293959
G RP VS A(2023)Evading Machine-Learning-Based Android Malware Detector for IoT DevicesIEEE Systems Journal10.1109/JSYST.2022.321501417:2(2745-2755)Online publication date: Jun-2023
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Lan TDarwaish ANait-Abdesselam FGu P(2023)Defensive Randomization Against Adversarial Attacks in Image-Based Android Malware DetectionICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10279592(5072-5077)Online publication date: 28-May-2023
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Bostani HMoonsamy V(2023)EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware DetectionComputers & Security10.1016/j.cose.2023.103676(103676)Online publication date: Dec-2023
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Guerra-Manzanares A(2023)Android malware detection: mission accomplished? A review of open challenges and future perspectivesComputers & Security10.1016/j.cose.2023.103654(103654)Online publication date: Dec-2023
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