Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913662377132032 |
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| author | He, Ping Cavallaro, Lorenzo Ji, Shouling |
| author_facet | He, Ping Cavallaro, Lorenzo Ji, Shouling |
| contents | Android malware presents a persistent threat to users' privacy and data integrity. To combat this, researchers have proposed machine learning-based (ML-based) Android malware detection (AMD) systems. However, adversarial Android malware attacks compromise the detection integrity of the ML-based AMD systems, raising significant concerns. Existing defenses against adversarial Android malware provide protections against feature space attacks which generate adversarial feature vectors only, leaving protection against realistic threats from problem space attacks which generate real adversarial malware an open problem. In this paper, we address this gap by proposing ADD, a practical adversarial Android malware defense framework designed as a plug-in to enhance the adversarial robustness of the ML-based AMD systems against problem space attacks. Our extensive evaluation across various ML-based AMD systems demonstrates that ADD is effective against state-of-the-art problem space adversarial Android malware attacks. Additionally, ADD shows the defense effectiveness in enhancing the adversarial robustness of real-world antivirus solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13782 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems He, Ping Cavallaro, Lorenzo Ji, Shouling Cryptography and Security Artificial Intelligence Machine Learning Software Engineering Android malware presents a persistent threat to users' privacy and data integrity. To combat this, researchers have proposed machine learning-based (ML-based) Android malware detection (AMD) systems. However, adversarial Android malware attacks compromise the detection integrity of the ML-based AMD systems, raising significant concerns. Existing defenses against adversarial Android malware provide protections against feature space attacks which generate adversarial feature vectors only, leaving protection against realistic threats from problem space attacks which generate real adversarial malware an open problem. In this paper, we address this gap by proposing ADD, a practical adversarial Android malware defense framework designed as a plug-in to enhance the adversarial robustness of the ML-based AMD systems against problem space attacks. Our extensive evaluation across various ML-based AMD systems demonstrates that ADD is effective against state-of-the-art problem space adversarial Android malware attacks. Additionally, ADD shows the defense effectiveness in enhancing the adversarial robustness of real-world antivirus solutions. |
| title | Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems |
| topic | Cryptography and Security Artificial Intelligence Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2501.13782 |