Visible to the public Detecting Authentication-Bypass Flaws in a Large Scale of IoT Embedded Web Servers

TitleDetecting Authentication-Bypass Flaws in a Large Scale of IoT Embedded Web Servers
Publication TypeConference Paper
Year of Publication2018
AuthorsJiang, Yikun, Xie, Wei, Tang, Yong
Conference NameProceedings of the 8th International Conference on Communication and Network Security
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-6567-3
KeywordsAutomated, composability, Embedded systems, IoT firmware, large scale, Metrics, pubcrawl, Resiliency, Router Systems Security, vulnerability detection
Abstract

With the rapid development of network and communication technologies, everything is able to be connected to the Internet. IoT devices, which include home routers, IP cameras, wireless printers and so on, are crucial parts facilitating to build pervasive and ubiquitous networks. As the number of IoT devices around the world increases, the security issues become more and more serious. To handle with the security issues and protect the IoT devices from being compromised, the firmware of devices needs to be strengthened by discovering and repairing vulnerabilities. Current vulnerability detection tools can only help strengthening traditional software, nevertheless these tools are not practical enough for IoT device firmware, because of the peculiarity in firmware's structure and embedded device's architecture. Therefore, new vulnerability detection framework is required for analyzing IoT device firmware. This paper reviews related works on vulnerability detection in IoT firmware, proposes and implements a framework to automatically detect authentication-bypass flaws in a large scale of Linux-based firmware. The proposed framework is evaluated with a data set of 2351 firmware images from several target vendors, which is proved to be capable of performing large-scale and automated analysis on firmware, and 1 known and 10 unknown authentication-bypass flaws are found by the analysis.

URLhttp://doi.acm.org/10.1145/3290480.3290491
DOI10.1145/3290480.3290491
Citation Keyjiang_detecting_2018