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Efficient concept drift handling for batch android malware detection models
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DOI：10.1016/j.pmcj.2023.101849.png)
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摘要
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The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature pays limited attention to addressing this issue, with many advanced Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid, relying on static models. In this work, we show how retraining techniques are able to maintain detector capabilities over time. Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: (1) the frequency with which the models are retrained, and (2) the data used for retraining. In the first experiment, we compare periodic retraining with a more advanced concept drift detection method that triggers retraining only when necessary. In the second experiment, we analyze sampling methods to reduce the amount of data used to retrain models. Specifically, we compare fixed sized windows of recent data and state-of-the-art active learning methods that select those apps that help keep the training dataset small but diverse. Our experiments show that concept drift detection and sample selection mechanisms result in very efficient retraining strategies which can be successfully used to maintain the performance of the static Android malware state-of-the-art detectors in changing environments.
Keyword:
Android malware detection
Machine learning
Mobile security
Concept drift
Static analysis
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3.5
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被引数:
2.2K
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引用论文
A Review of Android Malware Detection Approaches Based on Machine Learning基于机器学习的Android恶意软件检测方法综述.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏On the relativity of time: Implications and challenges of data drift on long-term effective android malware detection关于时间的相关性: 数据漂移对长期有效的android恶意软件检测的影响和挑战COMPUTERS & SECURITY
COMPUTERS & SECURITYIF5.4
PREAI
分享收藏From concept drift to model degradation: An overview on performance-aware drift detectors从概念漂移到模型退化: 性能感知漂移检测器综述OAAI
OAAI分享收藏MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version)MaMaDroid: 通过构建行为模型的马尔可夫链来检测Android恶意软件 (扩展版本)
PREAI
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