Android Malware Detection Based on Heterogeneous Information Network with Cross-Layer Features

Ren Xixuan, Zhao Lirui, Wang Kai, Xue Zhixing, Hou Anran, Shao Qiao
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Abstract

As a mature and open mobile operating system, Android runs on many IoT devices, which has led to Android-based IoT devices have become a hotbed of malware. Existing static detection methods for malware using artificial intelligence algorithms focus only on the java code layer when extracting API features, however there is a lot of malicious behavior involving native layer code. Thus, to make up for the neglect of the native code layer, we propose a heterogeneous information network-based Android malware detection method with cross-layer features. We first translate the semantic information of apps and API calls into the form of meta-paths, and construct the adjacency of apps based on API calls, then combine information from different meta-paths using multi-core learning. We implemented our method on the dataset from VirusShare and AndroZoo, and the experimental results show that the accuracy of our method is 93.4%, which is at least 2% higher than other related methods using heterogeneous information networks for malware detection.
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基于跨层异构信息网络的Android恶意软件检测
Android作为一个成熟开放的移动操作系统,运行在许多物联网设备上,这导致基于Android的物联网设备成为恶意软件的温床。现有的基于人工智能算法的恶意软件静态检测方法在提取API特征时只关注java代码层,而大量恶意行为涉及原生层代码。因此,为了弥补对本机代码层的忽视,我们提出了一种基于异构信息网络的具有跨层特征的Android恶意软件检测方法。我们首先将应用程序和API调用的语义信息转换成元路径的形式,并基于API调用构建应用程序的邻接关系,然后利用多核学习将来自不同元路径的信息进行组合。我们在VirusShare和AndroZoo的数据集上实现了我们的方法,实验结果表明,我们的方法的准确率为93.4%,比其他使用异构信息网络进行恶意软件检测的相关方法高出至少2%。
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