Embedded systems are omnipresent in our everyday life and are becoming increasingly present in many computing and networked environments. For example, they are at the core of various Common-Off-The-Shelf (COTS) devices such as printers, video surveillance systems, home routers and virtually anything we informally call electronics. The emerging phenomenon of the Internet-of-Things (IoT) will make them even more widespread and interconnected. Cisco famously predicted that there will be 50 billion connected embedded devices by 2020. Given those estimations, the heterogeneity of technology and application fields, and the current threat landscape, the security of all those devices becomes of paramount importance. In addition to this, manual security analysis does not scale. Therefore, novel, scalable and automated approaches are needed. In this talk, we present several methods that make *the large scale security analyses of embedded devices* a feasible task. We implemented those techniques in a scalable framework that we tested on real world data. First, we collected a large number of firmware images from Internet repositories and then performed simple static analysis. Second, since embedded devices often expose web interfaces for remote administration, therefore we developed techniques for large scale static and dynamic analysis of such interfaces. Finally, identifying and classifying the firmware files, as well as fingerprinting and identifying embedded devices is difficult, especially at large scale. Using these techniques, we were able to discover a large number of new vulnerabilities in dozens of firmware packages, affecting a great variety of vendors and device classes. We were also able to achieve high accuracy in fingerprinting and classification of both firmware images and live devices.
This material is both important and innovative because it addresses the more and more pressing matter of securing/hacking the IoT, hence the embedded devices themselves. Moreover, it's envisioned that the topic will go in the next few years from important to critically important. This material is definitely innovative because it will provide technical aspects of security research by combining several interesting research directions: - non-x86 emulation (and the caveats) - some aspects of static and dynamic analysis - machine learning - device fingerprinting This material is significant because it will present the results, knowledge and insights that resulted from three years of experimentation and work using systematic, methodological and academic approaches. Audience will be presented with technical knowledge, demos, insights, lessons learned and open challenges.
Speakers: Andrei Costin