Biblio

The USB protocol has become ubiquitous, supporting devices from high-powered computing devices to small embedded devices and control systems. USB's greatest feature, its openness and expandability, is also its weakness, and attacks such as BadUSB exploit the unconstrained functionality afforded to these devices as a vector for compromise. Fundamentally, it is virtually impossible to know whether a USB device is benign or malicious. This work introduces FirmUSB, a USB-specific firmware analysis framework that uses domain knowledge of the USB protocol to examine firmware images and determine the activity that they can produce. Embedded USB devices use microcontrollers that have not been well studied by the binary analysis community, and our work demonstrates how lifters into popular intermediate representations for analysis can be built, as well as the challenges of doing so. We develop targeting algorithms and use domain knowledge to speed up these processes by a factor of 7 compared to unconstrained fully symbolic execution. We also successfully find malicious activity in embedded 8051 firmwares without the use of source code. Finally, we provide insights into the challenges of symbolic analysis on embedded architectures and provide guidance on improving tools to better handle this important class of devices.

Text messaging is used by more people around the world than any other communications technology. As such, it presents a desirable medium for spammers. While this problem has been studied by many researchers over the years, the recent increase in legitimate bulk traffic (e.g., account verification, 2FA, etc.) has dramatically changed the mix of traffic seen in this space, reducing the effectiveness of previous spam classification efforts. This paper demonstrates the performance degradation of those detectors when used on a large-scale corpus of text messages containing both bulk and spam messages. Against our labeled dataset of text messages collected over 14 months, the precision and recall of past classifiers fall to 23.8% and 61.3% respectively. However, using our classification techniques and labeled clusters, precision and recall rise to 100% and 96.8%. We not only show that our collected dataset helps to correct many of the overtraining errors seen in previous studies, but also present insights into a number of current SMS spam campaigns.

Defenders of enterprise networks have a critical need to quickly identify the root causes of malware and data leakage. Increasingly, USB storage devices are the media of choice for data exfiltration, malware propagation, and even cyber-warfare. We observe that a critical aspect of explaining and preventing such attacks is understanding the provenance of data (i.e., the lineage of data from its creation to current state) on USB devices as a means of ensuring their safe usage. Unfortunately, provenance tracking is not offered by even sophisticated modern devices. This work presents ProvUSB, an architecture for fine-grained provenance collection and tracking on smart USB devices. ProvUSB maintains data provenance by recording reads and writes at the block layer and reliably identifying hosts editing those blocks through attestation over the USB channel. Our evaluation finds that ProvUSB imposes a one-time 850 ms overhead during USB enumeration, but approaches nearly-bare-metal runtime performance (90% of throughput) on larger files during normal execution, and less than 0.1% storage overhead for provenance in real-world workloads. ProvUSB thus provides essential new techniques in the defense of computer systems and USB storage devices.