With the increasing prevalence of fraudulent Android applications such as fake and malicious applications, it is crucial to detect them with high accuracy and adaptability. This paper introduces AgentDroid, a novel framework for Android fraudulent application detection based on multi-modal analysis and multi-agent systems. AgentDroid overcomes the limitations of traditional detection methods such as the inability to handle multimodal data and high false alarm rates. It processes Android applications and extracts a series of multi-modal data for analysis. Multiple LLM-based agents with specialized roles analyze the relevant data and collaborate to detect complex fraud effectively. We constructed a dataset containing various categories of fraudulent applications and legitimate applications and validated our framework on this dataset. Experimental results indicate that our multi-agent framework based on GPT-4o achieves an accuracy of 91.7% and an F1-Score of 91.68%, showing improved detection accuracy over the baseline methods.

翻译：随着欺诈性Android应用（如仿冒和恶意应用）日益普遍，以高准确性和适应性检测它们变得至关重要。本文介绍了AgentDroid，一种基于多模态分析和多智能体系统的Android欺诈应用检测新框架。AgentDroid克服了传统检测方法（如无法处理多模态数据和高误报率）的局限性。它处理Android应用并提取一系列多模态数据进行分析。多个具有专门角色的基于LLM的智能体分析相关数据并协作以有效检测复杂欺诈。我们构建了一个包含各类欺诈性应用和合法应用的数据集，并在该数据集上验证了我们的框架。实验结果表明，我们基于GPT-4o的多智能体框架实现了91.7%的准确率和91.68%的F1分数，显示出相较于基线方法检测准确率的提升。