LAMD: Context-driven Android Malware Detection and Classification with LLMs
The rapid growth of mobile applications has escalated Android malware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset biases, and limited explainability. Large Language Models (LLMs) offer a promising alternative with their zero-shot inference and reasoning capabilities. However, applying LLMs to Android malware detection presents two key challenges: (1)the extensive support code in Android applications, often spanning thousands of classes, exceeds LLMs' context limits and obscures malicious behavior within benign functionality; (2)the structural complexity and interdependencies of Android applications surpass LLMs' sequence-based reasoning, fragmenting code analysis and hindering malicious intent inference. To address these challenges, we propose LAMD, a practical context-driven framework to enable LLM-based Android malware detection. LAMD integrates key context extraction to isolate security-critical code regions and construct program structures, then applies tier-wise code reasoning to analyze application behavior progressively, from low-level instructions to high-level semantics, providing final prediction and explanation. A well-designed factual consistency verification mechanism is equipped to mitigate LLM hallucinations from the first tier. Evaluation in real-world settings demonstrates LAMD's effectiveness over conventional detectors, establishing a feasible basis for LLM-driven malware analysis in dynamic threat landscapes.
Code (0)
등록된 구현이 없습니다.
Tasks
Android Malware DetectionMalware AnalysisMalware DetectionSimilar Papers 제목 키워드 기반
LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis
Machine learning (ML)-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributions evolve due to frequent changes in th…
Malware DetectionAdversarial Samples on Android Malware Detection Systems for IoT Systems
Many IoT(Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gr…
Android Malware DetectionMalware DetectionUnraveling 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 …
Android Malware DetectionFeature EngineeringMalware DetectionLLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors
The rapid growth in both the scale and complexity of Android malware has driven the widespread adoption of machine learning (ML) techniques for scalable and accurate malware detection. Despite their effectiveness, these …
Adversarial AttackMalware DetectionRevisiting Static Feature-Based Android Malware Detection
The increasing reliance on machine learning (ML) in computer security, particularly for malware classification, has driven significant advancements. However, the replicability and reproducibility of these results are oft…
Android Malware DetectionComputer SecurityContinual LearningMalware Analysis+2