Moirae：面向动态 Android 恶意软件检测的多模态智能体协作框架

Computer Science > Cryptography and Security arXiv:2608.27994 (cs) [Submitted on 28 Aug 2026 (v1), last revised 15 Sep 2026 (this version, v2)] Title:Moirae: A Multimodal Agent Collaborative Framework for Dynamic Android Malware Detection Authors:Xueying Zeng, Youquan Xian, Yanze Li, Bowen Hu, Ziqi Shan, Xu Luo, Danping Yang, Peng Liu, Lei Cui, Bo Li View a PDF of the paper titled Moirae: A Multimodal Agent Collaborative Framework for Dynamic Android Malware Detection, by Xueying Zeng and 9 other authors View PDF HTML (experimental) Abstract:The Android ecosystem faces persistent and rapidly evolving malware threats. Existing machine learning detectors are vulnerable to concept drift because they rely on implementation-specific features whose distributions change over time. Large language models (LLMs) offer strong semantic understanding and zero-shot reasoning, but current LLM-based detectors typically depend on code-centric or single-dimensional evidence, making them susceptible to obfuscation and limiting comprehensive behavior analysis. We present {\sysname}, a multimodal agent collaborative framework for dynamic Android malware detection. {\sysname} dynamically collects multimodal runtime evidence and employs ReAct-based specialized agents to analyze complementary behavioral views. The detection process begins by identifying visual deception cues, modeling UI state transitions, and integrating runtime API behaviors to fuse multi-dimensional evidence across user-visible interfaces and hidden backend operations. Experiments on temporally and distributionally unseen datasets show that {\sysname} achieves an accuracy of 90.06\% without fine-tuning, outperforming state-of-the-art baselines and demonstrating strong zero-shot generalization against Android malware concept drift. Subjects: Cryptography and Security (cs.CR); Software Engineering (cs.SE) Cite as: arXiv:2608.27994 [cs.CR] (or arXiv:2608.27994v2 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.27994 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xueying Zeng [view email] [v1] Fri, 28 Aug 2026 07:03:38 UTC (1,448 KB) [v2] Tue, 15 Sep 2026 15:51:44 UTC (1,448 KB) Full-text links: Access Paper: View a PDF of the paper titled Moirae: A Multimodal Agent Collaborative Framework for Dynamic Android Malware Detection, by Xueying Zeng and 9 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR new | recent | 2026-08 Change to browse by: cs cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)