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Malware Detection Based on Deep Learning of Behavior Graphs

机译：基于行为图深度学习的恶意软件检测

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摘要

The Internet of Things (IoT) provides various benefits, which makes smart device even closer. With more and more smart devices in IoT, security is not a one-device affair. Many attacks targeted at traditional computers in IoT environment may also aim at other IoT devices. In this paper, we consider an approach to protect IoT devices from being attacked by local computers. In response to this issue, we propose a novel behavior-based deep learning framework (BDLF) which is built in cloud platform for detecting malware in IoT environment. In the proposed BDLF, we first construct behavior graphs to provide efficient information of malware behaviors using extracted API calls. We then use a neural network-Stacked AutoEncoders (SAEs) for extracting high-level features from behavior graphs. The layers of SAEs are inserted one after another and the last layer is connected to some added classifiers. The architecture of the SAEs is 6,000-2,000-500. The experiment results demonstrate that the proposed BDLF can learn the semantics of higher-level malicious behaviors from behavior graphs and further increase the average detection precision by 1.5%.
机译：物联网（IoT）提供了各种好处，使智能设备更加接近。随着物联网中越来越多的智能设备，安全性不再是一台设备。许多针对物联网环境中传统计算机的攻击也可能针对其他物联网设备。在本文中，我们考虑了一种保护IoT设备免受本地计算机攻击的方法。针对这一问题，我们提出了一种新颖的基于行为的深度学习框架（BDLF），该框架内置于云平台中，用于检测物联网环境中的恶意软件。在提出的BDLF中，我们首先构造行为图，以使用提取的API调用提供恶意软件行为的有效信息。然后，我们使用神经网络堆叠式自动编码器（SAE）从行为图中提取高级特征。一层又一层地插入SAE，最后一层连接到一些添加的分类器。 SAE的体系结构为6,000-2,000-500。实验结果表明，提出的BDLF可以从行为图中学习更高级别的恶意行为的语义，并进一步提高平均检测精度1.5％。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第4期|8195395.1-8195395.10|共10页
  • 作者

    Xiao Fei; Lin Zhaowen; Sun Yi; Ma Yan;

  • 作者单位

    Beijing Univ Posts & Telecommun, Inst Network Technol, Network & Informat Ctr, Beijing 100876, Peoples R China|Sci & Technol Informat Transmiss & Disseminat Com, Shijiazhuang 050081, Hebei, Peoples R China|Beijing Univ Posts & Telecommun, Natl Engn Lab Mobile Network Secur, Beijing 100876, Peoples R China;

    Beijing Univ Posts & Telecommun, Inst Network Technol, Network & Informat Ctr, Beijing 100876, Peoples R China|Sci & Technol Informat Transmiss & Disseminat Com, Shijiazhuang 050081, Hebei, Peoples R China|Beijing Univ Posts & Telecommun, Natl Engn Lab Mobile Network Secur, Beijing 100876, Peoples R China;

    Sci & Technol Informat Transmiss & Disseminat Com, Shijiazhuang 050081, Hebei, Peoples R China|Beijing Univ Posts & Telecommun, Natl Engn Lab Mobile Network Secur, Beijing 100876, Peoples R China|Beijing Univ Posts & Telecommun, Inst Sensing Technol & Business, Inst Network Technol, Network & Informat Ctr, Beijing 100000, Peoples R China;

    Beijing Univ Posts & Telecommun, Inst Network Technol, Network & Informat Ctr, Beijing 100876, Peoples R China;

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