论文标题
护士：智能家居的最终用户IoT IoT恶意软件检测工具
NURSE: eNd-UseR IoT malware detection tool for Smart homEs
论文作者
论文摘要
传统的检测恶意软件感染的技术并不是最终用户使用的，而当前的恶意软件拆卸工具和安全软件无法处理物联网设备的异质性。在本文中，我们设计，开发和评估一种称为护士的工具，以填补此信息空白，即使最终用户能够检测其家庭网络中的物联网 - malware感染。护士采用模块化方法来分析通过ARP欺骗技术捕获的物联网流量，该技术不需要任何网络修改或特定的硬件。因此，护士在每个人的范围内提供零配置的IOT流量分析。在具有多种物联网设备类型的83种不同的物联网网络方案中测试护士之后，结果表明，护士使用设备网络行为和接触的目的地来识别具有高精度（86.7％）的恶意软件的物联网设备。
Traditional techniques to detect malware infections were not meant to be used by the end-user and current malware removal tools and security software cannot handle the heterogeneity of IoT devices. In this paper, we design, develop and evaluate a tool, called NURSE, to fill this information gap, i.e., enabling end-users to detect IoT-malware infections in their home networks. NURSE follows a modular approach to analyze IoT traffic as captured by means of an ARP spoofing technique which does not require any network modification or specific hardware. Thus, NURSE provides zero-configuration IoT traffic analysis within everybody's reach. After testing NURSE in 83 different IoT network scenarios with a wide variety of IoT device types, results show that NURSE identifies malware-infected IoT devices with high accuracy (86.7%) using device network behavior and contacted destinations.
