research-article

Leveraging Reinforcement Learning and Generative Adversarial Networks to Craft Mutants of Windows Malware against Black-box Malware Detectors

Authors:

Tran Duc Luong,

Nguyen Hoang Quoc An,

Quyen Nguyen Huu,

Hoang Khoa Nghi,

Van-Hau PhamAuthors Info & Claims

SoICT '22: Proceedings of the 11th International Symposium on Information and Communication Technology

Pages 31 - 38

https://doi.org/10.1145/3568562.3568636

Published: 01 December 2022 Publication History

Abstract

To build an effective malware detector, it is required to collect a diversity of malware samples and their evolution, since malware authors always try to evade detectors through strategies of malware mutation. So, this paper explores the ability to craft mutants of malware for gathering numerous mutated samples in training a machine learning (ML)-based malware detector. Specifically, we leverage Reinforcement Learning (RL) and Generative Adversarial Networks (GAN) to generate adversarial malware samples against ML-based detectors. The more we use this approach with different targeted antivirus and malware samples in training the RL agent as a malware mutator, the more it learns how to avoid black box malware detectors. The experimental results in real-world dataset indicate that RL can help GAN in crafting variants of malware with executability preservation to evade ML-based detectors and VirusTotal. Finally, this approach can be used as an automated tool for benchmarking the robustness of malware detectors against the metamorphic malwares.

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Cited By

Achuthan KRamanathan SSrinivas SRaman R(2024)Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directionsFrontiers in Big Data10.3389/fdata.2024.14975357Online publication date: 5-Dec-2024
https://doi.org/10.3389/fdata.2024.1497535
Khalifa MAlmomani IEl-Shafai W(2024)SAMA: A Comprehensive Smart Automated Malware Analyzer Empowered by ChatGPT Integration2024 IEEE 30th International Conference on Telecommunications (ICT)10.1109/ICT62760.2024.10606026(1-6)Online publication date: 24-Jun-2024
https://doi.org/10.1109/ICT62760.2024.10606026
Ilca LLucian OBalan T(2023)Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and ResponseSensors10.3390/s2315675723:15(6757)Online publication date: 28-Jul-2023
https://doi.org/10.3390/s23156757
Show More Cited By

Index Terms

Leveraging Reinforcement Learning and Generative Adversarial Networks to Craft Mutants of Windows Malware against Black-box Malware Detectors
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Malware and its mitigation

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SoICT '22: Proceedings of the 11th International Symposium on Information and Communication Technology

December 2022

474 pages

ISBN:9781450397254

DOI:10.1145/3568562

Copyright © 2022 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 ACM 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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Published: 01 December 2022

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SoICT 2022

SoICT 2022: The 11th International Symposium on Information and Communication Technology

December 1 - 3, 2022

Hanoi, Vietnam

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Cited By

Achuthan KRamanathan SSrinivas SRaman R(2024)Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directionsFrontiers in Big Data10.3389/fdata.2024.14975357Online publication date: 5-Dec-2024
https://doi.org/10.3389/fdata.2024.1497535
Khalifa MAlmomani IEl-Shafai W(2024)SAMA: A Comprehensive Smart Automated Malware Analyzer Empowered by ChatGPT Integration2024 IEEE 30th International Conference on Telecommunications (ICT)10.1109/ICT62760.2024.10606026(1-6)Online publication date: 24-Jun-2024
https://doi.org/10.1109/ICT62760.2024.10606026
Ilca LLucian OBalan T(2023)Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and ResponseSensors10.3390/s2315675723:15(6757)Online publication date: 28-Jul-2023
https://doi.org/10.3390/s23156757
Arif RAslam MAl-Otaibi SMartinez-Enriquez ASaba TBahaj SRehman A(2023)A Deep Reinforcement Learning Framework to Evade Black-Box Machine Learning Based IoT Malware Detectors Using GAN-Generated Influential FeaturesIEEE Access10.1109/ACCESS.2023.333464511(133717-133729)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3334645
Rigaki MGarcia S(2023)The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement LearningComputer Security – ESORICS 202310.1007/978-3-031-51482-1_3(44-64)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-51482-1_3

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