基于改进人工蜂群算法的Android恶意应用检测

摘要/Abstract

摘要： 随着互联网和移动终端的飞速发展,手机中存储着很多重要的信息,要保证这些信息安全不被泄露的一个重要方法就是对手机中的恶意应用进行检测与处理。在对恶意应用进行检测前需要对样本进行特征提取,而如何在众多特征中进行有效的选取是恶意应用检测中一个至关重要的过程。文中针对Android平台的应用,参考相关的Android恶意检测方法,建立了一个基于改进人工蜂群算法的Android恶意应用检测模型,通过对特征进行有效的选择,最终得到使分类结果最优的特征组合,从而提高对Android恶意应用检测的检测性能。在静态和动态条件下分别对Android应用特征进行提取,通过多种分类算法对恶意应用检测模型进行检验,结果证实提出的基于改进人工蜂群算法的Android恶意应用检测方法具有可行性与优越性。

关键词: 恶意应用检测, 人工蜂群, 特征选取, 特征优化

Abstract: With the rapid development of the Internet and mobile terminals,there are a lot of important information stored in mobile phones.An important way to ensure that these information is not compromised is to detect and process malicious applications in mobile phones.Before detecting malicious applications,feature extraction is required for samples,and how to effectively select features among many features is a crucial process in malicious application detection.Based on the application of Android platform,this paper established an Android malicious application detection model based on the improved artificial bee colony algorithm.By effectively selecting the features,the feature combination that optimizes the classification results is finally obtained,thereby improving the detection performance of Android malicious application detection.The Android application features are extracted under static and dynamic conditions respectively.The malicious application detection model is tested by various classification algorithms.It is proved that the proposed malicious malicious detection method based on the improved artificial bee colony algorithm has the feasibility and superiority.

Key words: Artificial bee colony classification, Feature optimization, Feature selection, Malicious application detection

中图分类号:

TP311

徐开勇, 肖警续, 郭松, 戴乐育, 段佳良. 基于改进人工蜂群算法的Android恶意应用检测[J]. 计算机科学, 2019, 46(11A): 421-427. https://doi.org/

XU Kai-yong, XIAO Jing-xu, GUO Song, DAI Le-yu, DUAN Jia-liang. Android Malicious Application Detection Based on Improved Artificial Bee Colony Algorithms[J]. Computer Science, 2019, 46(11A): 421-427. https://doi.org/

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