Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy, Changsha, China.
Big Data Intelligence Police Hunan Provincial Engineering Research Center, Hunan Police Academy, Changsha, China.
Comput Math Methods Med. 2020 Mar 10;2020:3658795. doi: 10.1155/2020/3658795. eCollection 2020.
Recently, brain-machine interfacing is very popular that link humans and artificial devices through brain signals which lead to corresponding mobile application as supplementary. The Android platform has developed rapidly because of its good user experience and openness. Meanwhile, these characteristics of this platform, which cause the amazing pace of Android malware, pose a great threat to this platform and data correction during signal transmission of brain-machine interfacing. Many previous works employ various behavioral characteristics to analyze Android application (or app) and detect Android malware to protect signal data secure. However, with the development of Android app, category of Android app tends to be diverse, and the Android malware behavior tends to be complex. This situation makes existing Android malware detections complicated and inefficient. In this paper, we propose a broad analysis, gathering as many behavior characteristics of an app as possible and compare these behavior characteristics in several metrics. First, we extract static and dynamic behavioral characteristic from Android app in an automatic manner. Second, we explain the decision we made in each kind of behavioral characteristic we choose for Android app analysis and Android malware detection. Third, we design a detailed experiment, which compare the efficiency of each kind of behavior characteristic in different aspects. The results of experiment also show Android malware detection performance of these behavior characteristics combine with well-known machine learning algorithms.
最近，脑机接口非常流行，它通过脑信号将人类和人工智能设备连接起来，从而实现相应的移动应用作为补充。Android 平台因其良好的用户体验和开放性而迅速发展。然而，这个平台的这些特点导致了 Android 恶意软件惊人的发展速度，对这个平台和脑机接口信号传输过程中的数据修正构成了巨大威胁。许多先前的工作采用各种行为特征来分析 Android 应用程序（或 app）并检测 Android 恶意软件，以保护信号数据的安全。然而，随着 Android 应用程序的发展，Android 应用程序的类别趋于多样化，而 Android 恶意软件的行为也趋于复杂。这种情况使得现有的 Android 恶意软件检测变得复杂和低效。在本文中，我们提出了一种广泛的分析方法，尽可能多地收集应用程序的行为特征，并在几个指标上比较这些行为特征。首先，我们以自动的方式从 Android 应用程序中提取静态和动态行为特征。其次，我们解释了在为 Android 应用程序分析和 Android 恶意软件检测选择各种行为特征时所做的决策。第三，我们设计了一个详细的实验，比较了在不同方面每种行为特征的效率。实验结果还表明，这些行为特征与著名的机器学习算法相结合，具有较好的 Android 恶意软件检测性能。
