AgentDroid: A Multi-Agent Framework for Detecting Fraudulent Android Applications

• URL: http://arxiv.org/abs/2503.12163v1
• Date: Sat, 15 Mar 2025 15:07:43 GMT
• Title: AgentDroid: A Multi-Agent Framework for Detecting Fraudulent Android Applications
• Authors: Ruwei Pan, Hongyu Zhang, Zhonghao Jiang, Ran Hou,
• Abstract summary: AgentDroid is a novel framework for Android fraudulent application detection based on multi-modal analysis and multi-agent systems.<n>It processes Android applications and extracts a series of multi-modal data for analysis.<n>Our framework achieves an accuracy of 91.7% and an F1-Score of 91.68%, showing improved detection accuracy over the baseline methods.
• Score: 8.108572518706566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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