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Effectiveness of Android Obfuscation on Evading Anti-malware

Published: 13 March 2018 Publication History

Abstract

Obfuscation techniques have been conventionally used for legitimate applications, including preventing application reverse engineering, tampering and protecting intellectual property. A malware author could also leverage these benign techniques to hide their malicious intents and evade anti-malware detection. As variants of known malware have been regularly found on the Google Play Store, transformed malware attacks are a real problem that security solutions today need to address. It has been proven that mainstream security tools installed on smartphones are mainly signature-based; our work focuses on evaluating the efficiency of a composite of obfuscation techniques in evading anti-malware detection. We further verified the trend of transformed malware in evading detection, with a larger and more updated database of known malware. This is also the first work to-date that presents the instability of some anti-malware tools (AMTs) against obfuscated malware. This work also proved that current mainstream AMTs do not build up resilience against obfuscation methods, but instead try to update the signature database on created variants.

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Cited By

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  • (2024)Survey of Malware Detection through Use of Neural Network Models2024 International Conference on Cyber-Physical Social Intelligence (ICCSI)10.1109/ICCSI62669.2024.10799495(1-7)Online publication date: 8-Nov-2024
  • (2024)Maloid-DS: Labeled Dataset for Android Malware ForensicsIEEE Access10.1109/ACCESS.2024.340021112(73481-73546)Online publication date: 2024
  • (2024)Erasing the Shadow: Sanitization of Images with Malicious Payloads Using Deep AutoencodersFoundations of Intelligent Systems10.1007/978-3-031-62700-2_11(115-125)Online publication date: 17-Jun-2024
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cover image ACM Conferences
CODASPY '18: Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy
March 2018
401 pages
ISBN:9781450356329
DOI:10.1145/3176258
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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New York, NY, United States

Publication History

Published: 13 March 2018

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Author Tags

  1. android
  2. malware
  3. obfuscation techniques

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CODASPY '18 Paper Acceptance Rate 23 of 110 submissions, 21%;
Overall Acceptance Rate 149 of 789 submissions, 19%

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Cited By

View all
  • (2024)Survey of Malware Detection through Use of Neural Network Models2024 International Conference on Cyber-Physical Social Intelligence (ICCSI)10.1109/ICCSI62669.2024.10799495(1-7)Online publication date: 8-Nov-2024
  • (2024)Maloid-DS: Labeled Dataset for Android Malware ForensicsIEEE Access10.1109/ACCESS.2024.340021112(73481-73546)Online publication date: 2024
  • (2024)Erasing the Shadow: Sanitization of Images with Malicious Payloads Using Deep AutoencodersFoundations of Intelligent Systems10.1007/978-3-031-62700-2_11(115-125)Online publication date: 17-Jun-2024
  • (2023)Task-Aware Meta Learning-Based Siamese Neural Network for Classifying Control Flow Obfuscated MalwareFuture Internet10.3390/fi1506021415:6(214)Online publication date: 14-Jun-2023
  • (2023)A Survey of Recent Advances in Deep Learning Models for Detecting Malware in Desktop and Mobile PlatformsACM Computing Surveys10.1145/363824056:6(1-41)Online publication date: 20-Dec-2023
  • (2023)Android Malware Detection Methods Based on Convolutional Neural Network: A SurveyIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32818337:5(1330-1350)Online publication date: Oct-2023
  • (2023)Strength and Limitations of Publicly Available Anti-Malware Tools Against Obfuscated Malware2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA)10.1109/ICIRCA57980.2023.10220600(1261-1266)Online publication date: 3-Aug-2023
  • (2023)WebAssembly diversification for malware evasionComputers and Security10.1016/j.cose.2023.103296131:COnline publication date: 1-Aug-2023
  • (2022)Towards Obfuscation Resilient Feature Design for Android Malware Detection-KTSODroidElectronics10.3390/electronics1124407911:24(4079)Online publication date: 8-Dec-2022
  • (2022)Towards Robust Detection of PDF-based MalwareProceedings of the Twelfth ACM Conference on Data and Application Security and Privacy10.1145/3508398.3519365(370-372)Online publication date: 14-Apr-2022
  • Show More Cited By

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