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通过实际用例教授和学习物联网网络安全和漏洞评估以及 Shodan 的使用。

Teaching and Learning IoT Cybersecurity andVulnerability Assessment with Shodan through Practical Use Cases.

机构信息

Department of Computer Engineering, Faculty of Computer Science, Universidade da Coruña, 15071 A Coruña, Spain.

Centro de investigación CITIC, Universidade da Coruña, 15071 A Coruña, Spain.

出版信息

Sensors (Basel). 2020 May 27;20(11):3048. doi: 10.3390/s20113048.

Abstract

Shodan is a search engine for exploring the Internet and thus finding connected devices. Its main use is to provide a tool for cybersecurity researchers and developers to detect vulnerable Internet-connected devices without scanning them directly. Due to its features, Shodan can be used for performing cybersecurity audits on Internet of Things (IoT) systems and devices used in applications that require to be connected to the Internet. The tool allows for detecting IoT device vulnerabilities that are related to two common cybersecurity problems in IoT: the implementation of weak security mechanisms and the lack of a proper security configuration. To tackle these issues, this article describes how Shodan can be used to perform audits and thus detect potential IoT-device vulnerabilities. For such a purpose, a use case-based methodology is proposed to teach students and users to carry out such audits and then make more secure the detected exploitable IoT devices. Moreover, this work details how to automate IoT-device vulnerability assessments through Shodan scripts. Thus, this article provides an introductory practical guide to IoT cybersecurity assessment and exploitation with Shodan.

摘要

Shodan 是一种用于探索互联网并因此找到连接设备的搜索引擎。它的主要用途是为网络安全研究人员和开发人员提供一种工具,用于在不直接扫描它们的情况下检测易受攻击的互联网连接设备。由于其功能,Shodan 可用于对物联网 (IoT) 系统和应用程序中需要连接到互联网的设备执行网络安全审核。该工具可用于检测与物联网中两个常见网络安全问题相关的 IoT 设备漏洞:安全机制实施薄弱和缺乏适当的安全配置。为了解决这些问题,本文描述了如何使用 Shodan 执行审核,从而检测潜在的 IoT 设备漏洞。为此,提出了一种基于用例的方法,以教导学生和用户执行此类审核,然后使检测到的可利用 IoT 设备更加安全。此外,本文详细介绍了如何通过 Shodan 脚本自动执行 IoT 设备漏洞评估。因此,本文提供了使用 Shodan 进行物联网网络安全评估和利用的入门实践指南。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2246/7309102/4630fa89e85f/sensors-20-03048-g001.jpg

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