Why Not Diversify Triggers? APK-Specific Backdoor Attack Against Android Malware Detection

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Abstract
Machine learning-based Android malware detection (AMD) models require abundant data for training robust app classifiers, creating vulnerability to poisoning attacks. Attackers inject poisoned samples into Android app markets, leading to the insertion of a backdoor into the model upon adoption in the training process. Subsequently, attackers can generate evasive malware by embedding a backdoor trigger in malware samples. Currently, research on backdoor attacks towards AMD has just begun to emerge. Existing attacks produce a fixed trigger and apply it to various malware. Once the trigger is discovered by static analysis methods (e.g., software similarity analysis), however, multiple malware carrying this trigger will be simultaneously exposed. To diversify the trigger, we propose an APK-Specific Backdoor Attack algorithm (ASBA), which trains a generative adversarial network to generate a specific trigger for every malware sample. Moreover, ASBA manages to make the generated triggers as different as possible, in order to further reduce the likelihood of malware being collectively captured. Extensive experiments have demonstrated that ASBA achieves a 94.6% average attack success rate (ASR) on three datasets, five feature extraction methods and three classification models. Furthermore, compared to state-of-the-art poisoning attack algorithms, ASBA produces more diverse and more effective triggers.
Funding Information
  • Hong Kong RGC Project (PolyU15231223)
  • Open Foundation of Key Laboratory of Cyberspace Security
  • Ministry of Education of China
  • Henan Key Laboratory of Network Cryptography (KLCS20240305)
  • National Natural Science Foundation of China (62502173)
  • Postdoctoral Fellowship Program of CPSF (GZB20240248)
  • China Postdoctoral Science Foundation (2024M751010)

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