一句话：将掩码图重建机制引入 GNN 检测框架，并辅以对比学习，使 Android 恶意软件检测兼具强判别力与对抗鲁棒性。
Android 恶意软件威胁严峻，自动化检测需求迫切。图表示（如函数调用图）在刻画应用行为方面发挥关键作用，但当前最先进的基于图的检测器虽性能出色，却易受对抗样本攻击——攻击者只需对正常恶意输入施加特定扰动即可逃避检测。已有防御手段通常是检测器的附加模块，依赖对抗样本的先验知识，难以有效防御未见类型的攻击。为此，作者提出 MaskDroid：在基于图神经网络（GNN）的框架中引入掩码机制，强制模型仅用随机选取的一小部分节点（例如 20%）重建整张输入图。该策略促使模型理解恶意语义并学习更稳定的表示，从而提升对抗鲁棒性。在此基础上，作者进一步引入对比模块，在捕捉图结构内依赖形式的稳定恶意语义的同时，令良性类与恶意类的表示更为紧凑，以增强其区分恶意软件、良性应用与对抗样本的判别能力。
Android malware attacks have posed a severe threat to mobile users, necessitating a significant demand for the automated detection system. Among the various tools employed in malware detection, graph representations (e.g., function call graphs) have played a pivotal role in characterizing the behaviors of Android apps. However, though achieving impressive performance in malware detection, current state-of-the-art graph-based malware detectors are vulnerable to adversarial examples. These adversarial examples are meticulously crafted by introducing specific perturbations to normal malicious inputs. To defend against adversarial attacks, existing defensive mechanisms are typically supplementary additions to detectors and exhibit significant limitations, often relying on prior knowledge of adversarial examples and failing to defend against unseen types of attacks effectively. In this paper, we propose MASKDROID, a powerful detector with a strong discriminative ability to identify malware and remarkable robustness against adversarial attacks. Specifically, we introduce a masking mechanism into the Graph Neural Network (GNN) based framework, forcing MASKDROID to recover the whole input graph using a small portion (e.g., 20%) of randomly selected nodes.This strategy enables the model to understand the malicious semantics and learn more stable representations, enhancing its robustness against adversarial attacks. While capturing stable malicious semantics in the form of dependencies inside the graph structures, we further employ a contrastive module to encourage MASKDROID to learn more compact representations for both the benign and malicious classes to boost its discriminative power in detecting malware from benign apps and adversarial examples.
@inproceedings{paperbot8292,
title = {MaskDroid: Robust Android Malware Detection with Masked Graph Representations},
author = {Jingnan Zheng and Jiahao Liu and An Zhang and Jun Zeng and Ziqi Yang and Zhenkai Liang and Tat-Seng Chua},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695008}
}