Design, Fabrication, and Run-time Strategies for Hardware-Assisted Security

Today, electronic computing devices are critically involved in our daily lives, basic infrastructure, and national defense systems. With the growing number of threats against them, hardware-based security features offer the best chance for building secure and trustworthy cyber systems. In this dissertation, we investigate ways of making hardware-based security into a reality with primary focus on two areas: Hardware Trojan Detection and Physically Unclonable Functions (PUFs). Hardware Trojans are malicious modifications made to original IC designs or layouts that can jeopardize the integrity of hardware and software platforms. Since most modern systems critically depend on ICs, detection of hardware Trojans has garnered significant interest in academia, industry, as well as governmental agencies. The majority of existing detection schemes focus on test-time because of the limited hardware resources available at run-time. In this dissertation, we explore innovative run-time solutions that utilize on-chip thermal sensor measurements and fundamental estimation/detection theory to expose changes in IC power/thermal profile caused by Trojan activation. The proposed solutions are low overhead and also generalizable to many other sensing modalities and problem instances. Simulation results using state-of-the-art tools on publicly available Trojan benchmarks verify that our approaches can detect Trojans quickly and with few false positives. Physically Unclonable Functions (PUFs) are circuits that rely on IC fabrication variations to generate unique signatures for various security applications such as IC authentication, anti-counterfeiting, cryptographic key generation, and tamper resistance. While the existence of variations has been well exploited in PUF design, knowledge of exactly how variations come into existence has largely been ignored. Yet, for several decades the Design-for-Manufacturability (DFM) community has actually investigated the fundamental sources of these variations. Furthermore, since manufacturing variations are often harmful to IC yield, the existing DFM tools have been geared towards suppressing them (counter-intuitive for PUFs). In this dissertation, we make several improvements over current state-of-the-art work in PUFs. First, our approaches exploit existing DFM models to improve PUFs at physical layout and mask generation levels. Second, our proposed algorithms reverse the role of standard DFM tools and extend them towards improving PUF quality without harming non-PUF portions of the IC. Finally, since our approaches occur after design and before fabrication, they are applicable to all types of PUFs and have little overhead in terms of area, power, etc. The innovative and unconventional techniques presented in this dissertation should act as important building blocks for future work in cyber security

Energy and Thermal-Aware Video Coding via Encoder/Decoder Workload Balancing

Even with consistent advances in storage and transmission capacity, video coding and compression are essential components of multimedia services. Traditional video coding paradigms result in excessive computation at either the encoder or decoder. However, several recent papers have proposed a hybrid PVC/DVC (Predictive/ Distributed Video Coding) codec which shares the video coding workload. In this paper, we propose a controller for such hybrid coders that considers energy and temperature to dynamically split the coding workload of a system comprised of one encoder and one decoder. Results show that the proposed controller results in more balanced energy utilization, improving overall system lifetime and reducing operating temperatures when compared to strictly PVC and DVC systems

Time is money, friend! Timing Side-channel Attack against Garbled Circuit Constructions

With the advent of secure function evaluation (SFE), distrustful parties can jointly compute on their private inputs without disclosing anything besides the results. Yao’s garbled circuit protocol has become an integral part of secure computation thanks to considerable efforts made to make it feasible, practical, and more efficient. These efforts have resulted in multiple optimizations on this primitive to enhance its performance by orders of magnitude over the last years. The advancement in protocols has also led to the development of general-purpose compilers and tools made available to academia and industry. For decades, the security of protocols offered in those tools has been assured with regard to sound proofs and the promise that during the computation, no information on parties’ input would be leaking. In a parallel effort, however, side-channel analysis (SCA) has gained momentum in connection with the real-world implementation of cryptographic primitives. Timing side-channel attacks have proven themselves effective in retrieving secrets from implementations, even through remote access to them. Nevertheless, the vulnerability of garbled circuit frameworks to timing attacks has, surprisingly, never been discussed in the literature. This paper introduces Goblin, the first timing attack against commonly employed garbled circuit frameworks. Goblin is a machine learning-assisted, non-profiling, single-trace timing SCA, which successfully recovers the garbler’s input during the computation under different scenarios, including various GC frameworks, benchmark functions, and the number of garbler’s input bits. Furthermore, we discuss Gob- lin’s success factors and countermeasures against that. In doing so, Goblin hopefully paves the way for further research in this matter

Temperature Tracking: An Innovative Run-Time Approach for Hardware Trojan Detection

The hardware Trojan threat has motivated development of Trojan detection schemes at all stages of the integrated circuit (IC) lifecycle. While the majority of existing schemes focus on ICs at test-time, there are many unique advantages offered by post-deployment/run-time Trojan detection. However, run-time approaches have been underutilized with prior work highlighting the challenges of implementing them with limited hardware resources. In this paper, we propose innovative low-overhead approaches for run-time Trojan detection which exploit the thermal sensors already available in many modern systems to detect deviations in power/thermal profiles caused by Trojan activation. Simulation results using state-of-the-art tools on publicly available Trojan benchmarks verify that our approaches can detect active Trojans quickly and with few false positives