Heng Li (Huazhong University of Science and Technology), Zhiyuan Yao (Huazhong University of Science and Technology), Bang Wu (Huazhong University of Science and Technology), Cuiying Gao (Huazhong University of Science and Technology), Teng Xu (Huazhong University of Science and Technology), Wei Yuan (Huazhong University of Science and Technology), Xiapu Luo (The Hong Kong Polytechnic University)

Adversarial example techniques have been demonstrated to be highly effective against Android malware detection systems, enabling malware to evade detection with minimal code modifications. However, existing adversarial example techniques overlook the process of malware generation, thus restricting the applicability of adversarial example techniques. In this paper, we investigate piggybacked malware, a type of malware generated in bulk by piggybacking malicious code into popular apps, and combine it with adversarial example techniques. Given a malicious code segment (i.e., a rider), we can generate adversarial perturbations tailored to it and insert them into any carrier, enabling the resulting malware to evade detection. Through exploring the mechanism by which adversarial perturbation affects piggybacked malware code, we propose an adversarial piggybacked malware generation method, which comprises three modules: Malicious Rider Extraction, Adversarial Perturbation Generation, and Benign Carrier Selection. Extensive experiments have demonstrated that our method can efficiently generate a large volume of malware in a short period, and significantly increase the likelihood of evading detection. Our method achieved an average attack success rate (ASR) of 88.3% on machine learning-based detection models (e.g., Drebin and MaMaDroid), and an ASR of 76% and 92% on commercial engines Microsoft and Kingsoft, respectively. Furthermore, we have explored potential defenses against our adversarial piggybacked malware.

View More Papers

Delay-allowed Differentially Private Data Stream Release

Xiaochen Li (University of Virginia), Zhan Qin (Zhejiang University), Kui Ren (Zhejiang University), Chen Gong (University of Virginia), Shuya Feng (University of Connecticut), Yuan Hong (University of Connecticut), Tianhao Wang (University of Virginia)

Read More

Kronos: A Secure and Generic Sharding Blockchain Consensus with...

Yizhong Liu (Beihang University), Andi Liu (Beihang University), Yuan Lu (Institute of Software Chinese Academy of Sciences), Zhuocheng Pan (Beihang University), Yinuo Li (Xi’an Jiaotong University), Jianwei Liu (Beihang University), Song Bian (Beihang University), Mauro Conti (University of Padua)

Read More

Rethink Custom Transformers for Binary Analysis

Heng Yin, Professor, Department of Computer Science and Engineering, University of California, Riverside

Read More

TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents

Chen Gong (University of Vriginia), Kecen Li (Chinese Academy of Sciences), Jin Yao (University of Virginia), Tianhao Wang (University of Virginia)

Read More