Biblio

ARM has become the leading processor architecture for mobile and IoT devices, while it has recently started claiming a bigger slice of the server market pie as well. As such, it will not be long before malware more regularly target the ARM architecture. Therefore, the stealthy operation of Virtual Machine Introspection (VMI) is an obligation to successfully analyze and proactively mitigate this growing threat. Stealthy VMI has proven itself perfectly suitable for malware analysis on Intel's architecture, yet, it often lacks the foundation required to be equally effective on ARM.

Embedded devices are becoming more widespread, interconnected, and web-enabled than ever. However, recent studies showed that embedded devices are far from being secure. Moreover, many embedded systems rely on web interfaces for user interaction or administration. Web security is still difficult and therefore the web interfaces of embedded systems represent a considerable attack surface. In this paper, we present the first fully automated framework that applies dynamic firmware analysis techniques to achieve, in a scalable manner, automated vulnerability discovery within embedded firmware images. We apply our framework to study the security of embedded web interfaces running in Commercial Off-The-Shelf (COTS) embedded devices, such as routers, DSL/cable modems, VoIP phones, IP/CCTV cameras. We introduce a methodology and implement a scalable framework for discovery of vulnerabilities in embedded web interfaces regardless of the devices' vendor, type, or architecture. To reach this goal, we perform full system emulation to achieve the execution of firmware images in a software-only environment, i.e., without involving any physical embedded devices. Then, we automatically analyze the web interfaces within the firmware using both static and dynamic analysis tools. We also present some interesting case-studies and discuss the main challenges associated with the dynamic analysis of firmware images and their web interfaces and network services. The observations we make in this paper shed light on an important aspect of embedded devices which was not previously studied at a large scale.

Once data is released to the Internet, there is little hope to successfully delete it, as it may have been duplicated, reposted, and archived in multiple places. This poses a significant threat to users' privacy and their right to permanently erase their very own data. One approach to control the implications on privacy is to assign a lifetime value to the published data and ensure that the data is no longer accessible after this point in time. However, such an approach suffers from the inability to successfully predict the right time when the data should vanish. Consequently, the author of the data can only estimate the correct time, which unfortunately can cause the premature or belated deletion of data. This paper tackles the problem of prefixed lifetimes in data deletion from a different angle and argues that alternative approaches are a desideratum for research. In our approach, we consider different criteria when data should be deleted, such as keeping data available as long as there is sufficient interest for it or untimely delete it in cases of excessive accesses. To assist the self-destruction of data, we propose a protocol and develop a prototype, called Neuralyzer, which leverages the caching mechanisms of the Domain Name System (DNS) to ensure the successful deletion of data. Our experimental results demonstrate that our approach can completely delete published data while at the same time achieving flexible expiration times varying from few days to several months depending on the users' interest.