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首页> 外文期刊>International Journal of Interactive Mobile Technologies >Optimizing Android Malware Detection Via Ensemble Learning
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Optimizing Android Malware Detection Via Ensemble Learning

机译：通过集合学习优化Android恶意软件检测

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Android operating system has become very popular, with the highest market share, amongst all other mobile operating systems due to its open source nature and users friendliness. This has brought about an uncontrolled rise in malicious applications targeting the Android platform. Emerging trends of Android malware are employing highly sophisticated detection and analysis avoidance techniques such that the traditional signature-based detection methods have become less potent in their ability to detect new and unknown malware. Alternative approaches, such as the Machine learning techniques have taken the lead for timely zero-day anomaly detections.? The study aimed at developing an optimized Android malware detection model using ensemble learning technique. Random Forest, Support Vector Machine, and k-Nearest Neighbours were used to develop three distinct base models and their predictive results were further combined using Majority Vote combination function to produce an ensemble model. Reverse engineering procedure was employed to extract static features from large repository of malware samples and benign applications. WEKA 3.8.2 data mining suite was used to perform all the learning experiments. The results showed that Random Forest had a true positive rate of 97.9%, a false positive rate of 1.9% and was able to correctly classify instances with 98%, making it a strong base model. The ensemble model had a true positive rate of 98.1%, false positive rate of 1.8% and was able to correctly classify instances with 98.16%. The finding shows that, although the base learners had good detection results, the ensemble learner produced a better optimized detection model compared with the performances of those of the base learners.
机译：Android操作系统已变得非常受欢迎，市场份额最高，其中包括其开放源自然和用户友好的所有其他移动操作系统。这引起了针对Android平台的恶意应用程序的不受控制的兴起。 Android恶意软件的新兴趋势正在采用高度复杂的检测和分析避免技术，使得传统的基于签名的检测方法在检测新的和未知恶意软件的能力方面变得不那么有效。替代方法，例如机器学习技术已经采取了及时零异常检测的铅。该研究旨在使用集合学习技术开发优化的Android恶意软件检测模型。随机森林，支持向量机和k-inteld邻居用于开发三种不同的基础模型，并且他们的预测结果进一步使用多数票组合函数来产生集合模型。采用逆向工程程序来提取来自恶意软件样本和良性应用的大型存储库的静态特征。 Weka 3.8.2数据采矿套件用于执行所有学习实验。结果表明，随机森林的真正阳性率为97.9％，假阳性率为1.9％，能够正确地分类98％的情况，使其成为强大的基础模型。该集合模型的真正阳性率为98.1％，假阳性率为1.8％，能够正确地分类98.16％的情况。该发现表明，尽管基础学习者有良好的检测结果，但是，与基础学习者的性能相比，集合学习者产生了更好的优化检测模型。

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