MASKDROID: Robust Android Malware Detection with Masked Graph Representations

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.

Unraveling the Key of Machine Learning Solutions for Android Malware Detection

Android malware detection serves as the front line against malicious apps. With the rapid advancement of machine learning (ML), ML-based Android malware detection has attracted increasing attention due to its capability of automatically capturing malicious patterns from Android APKs. These learning-driven methods have reported promising results in detecting malware. However, the absence of an in-depth analysis of current research progress makes it difficult to gain a holistic picture of the state of the art in this area. This paper presents a comprehensive investigation to date into ML-based Android malware detection with empirical and quantitative analysis. We first survey the literature, categorizing contributions into a taxonomy based on the Android feature engineering and ML modeling pipeline. Then, we design a general-propose framework for ML-based Android malware detection, re-implement 12 representative approaches from different research communities, and evaluate them from three primary dimensions, i.e., effectiveness, robustness, and efficiency. The evaluation reveals that ML-based approaches still face open challenges and provides insightful findings like more powerful ML models are not the silver bullet for designing better malware detectors. We further summarize our findings and put forth recommendations to guide future research.

Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment

The rise of deep learning technique has raised new privacy concerns about the training data and test data. In this work, we investigate the model inversion problem in the adversarial settings, where the adversary aims at inferring information about the target model's training data and test data from the model's prediction values. We develop a solution to train a second neural network that acts as the inverse of the target model to perform the inversion. The inversion model can be trained with black-box accesses to the target model. We propose two main techniques towards training the inversion model in the adversarial settings. First, we leverage the adversary's background knowledge to compose an auxiliary set to train the inversion model, which does not require access to the original training data. Second, we design a truncation-based technique to align the inversion model to enable effective inversion of the target model from partial predictions that the adversary obtains on victim user's data. We systematically evaluate our inversion approach in various machine learning tasks and model architectures on multiple image datasets. Our experimental results show that even with no full knowledge about the target model's training data, and with only partial prediction values, our inversion approach is still able to perform accurate inversion of the target model, and outperform previous approaches.