The Internet of Things (IoT) is growing at a rapid pace and it is essential that the communication between these resource constrained IoT devices is secure and efficient. Traditional authentication schemes like RSA and ECDSA do not conform to the high throughput and time critical needs of these devices. In this research, we propose a hardware accelerated lattice based authentication scheme tailored specifically towards constrained IoT devices. We show that our scheme provides lower latency and higher throughput compared to traditional authentication schemes and its CPU only counter-parts.
Memory forensics can reveal “up to the minute” evidence of a device’s usage, often without requiring a suspect’s password to unlock the device, and it is oblivious to any persistent storage encryption schemes, e.g., whole disk encryption. Prior to my work, researchers and investigators alike considered data-structure recovery the ultimate goal of memory image forensics. This, however, was far from sufficient, as investigators were still largely unable to understand the content of the recovered evidence, and hence efficiently locating and accurately analyzing such evidence locked in memory images remained an open research challenge. In this dissertation, I propose breaking from traditional data-recovery-oriented forensics, and instead I present a memory forensics framework which leverages pro- gram analysis to automatically recover spatial-temporal evidence from memory im- ages by understanding the programs that generated it. This framework consists of four techniques, each of which builds upon the discoveries of the previous, that repre- sent this new paradigm of program-analysis-driven memory forensics. First, I present DSCRETE, a technique which reuses a program’s own interpretation and rendering logic to recover and present in-memory data structure contents. Following that, VCR developed vendor-generic data structure identification for the recovery of in-memory photographic evidence produced by an Android device’s cameras. GUITAR then re- alized an app-independent technique which automatically reassembles and redraws an app’s GUI from the multitude of GUI data elements found in a smartphone’s memory image. Finally, different from any traditional memory forensics technique, ix RetroScope introduced the vision of spatial-temporal memory forensics by retarget- ing an Android app’s execution to recover sequences of previous GUI screens, in their original temporal order, from a memory image. This framework, and the new program analysis techniques which enable it, have introduced encryption-oblivious forensics capabilities far exceeding traditional data-structure recovery.