一种新的基于包装的特征选择技术和烟花算法用于Android恶意软件检测

Mohamed Guendouz, Abdelmalek Amine
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引用次数: 4

摘要

近年来,智能手机的使用急剧扩大,尤其是基于android的智能手机,因为它们的广泛可用性和与非android设备相比具有竞争力的价格。Android应用程序使用的显著增加导致恶意应用程序数量激增,这对用户隐私构成了严重威胁。本文提出了一种基于fireworks算法的基于特征选择的Android恶意软件检测新方法FWA-FS。所提出的技术使用静态分析将应用程序分类为良性或恶意。为了描述应用程序,他们使用来自APK文件的权限作为特征向量。然后使用提出的FWA-FS方法选择最重要的特征。最后,为了开发分类模型,使用指定的特征训练不同的机器学习算法。实验结果表明,本文提出的策略可以大大提高KNN和Naïve贝叶斯的分类性能,准确率平均分别提高6%和25%。
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A New Wrapper-Based Feature Selection Technique with Fireworks Algorithm for Android Malware Detection
Smartphone use has expanded dramatically in recent years, particularly for Android-based smartphones, due to their wide availability and competitive pricing compared to non-Android devices. The significant increase in the use of Android applications has resulted in a spike in the number of malicious applications, which represent a severe danger to user privacy. In this paper, the authors proposed FWA-FS, a novel method for Android malware detection with feature selection based on the fireworks algorithm. Static analysis is used in the proposed technique to classify applications as benign or malicious. To describe applications, they employ permissions derived from APK files as feature vectors. The most important features were then chosen using the proposed FWA-FS method. Finally, to develop classification models, different machine learning algorithms were trained using specified features. According to experimental findings, the suggested strategy can greatly enhance classification performance with an average increase of 6% and 25% in accuracy for KNN and Naïve Bayes respectively.
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