HYDRA: Proactive Android Malware Drift Adaptation via Hierarchical Graph Contrastive Learning

2026-09-22

AI Summary

为解决Android恶意软件快速演变导致的概念漂移问题，HYDRA采用层次图对比学习方法主动适应变化，减少了对标注样本的需求，并提高了检测准确性。

Abstract

Concept drift, driven by the rapid evolution of Android malware, severely degrades the performance of machine learning detectors. Current adaptation strategies are often reactive, responding only after performance has dropped and imposing a significant manual annotation burden, or they are proactive but rely on unstable adversarial training and incomplete, single-level graph representations. To overcome these limitations, we propose HYDRA (Hybrid Drift Adaptation), a proactive adaptation framework that learns drift-invariant representations from hierarchically structured data. HYDRA first models applications using a hybrid graph structure, combining fine-grained Control Flow Graphs (CFGs) and coarse-grained Function Call Graphs (FCGs) to capture comprehensive behavioral patterns. It then introduces a novel cross-domain contrastive learning objective that aligns historical (source) and new (target) data distributions. By generating pseudo-labels for unlabeled target samples, our method pulls representations of semantically similar applications together, regardless of their domain, within a single, stable optimization process. This approach unifies feature learning and domain alignment, eliminating the need for complex adversarial objectives. Extensive experiments on large-scale, time-ordered malware datasets demonstrate that HYDRA achieves substantially lower False Negative and False Positive Rates than state-of-the-art baselines while requiring up to 87.5% fewer labeled samples. Our work thus offers a robust and efficient solution to combat concept drift in security applications.

Problem

• Concept Drift
• Android Malware
• Machine Learning Detectors
• Hierarchical Graph

Innovation

• Hierarchical Graph Contrastive Learning
• Hybrid Drift Adaptation
• Cross-domain Contrastive Learning