Hardware reverse engineering is a powerful and universal tool for both security engineers and adversaries. From a defensive perspective, it allows for detection of intellectual property infringements and hardware Trojans, while it simultaneously can be used for product piracy and malicious circuit manipulations. From a designer's perspective, it is crucial to have an estimate of the costs associated with reverse engineering, yet little is known about this, especially when dealing with obfuscated hardware. The contribution at hand provides new insights into this problem, based on algorithms with sound mathematical underpinnings. Our contributions are threefold: First, we present the graph similarity problem for automating hardware reverse engineering. To this end, we improve several state-of-the-art graph similarity heuristics with optimizations tailored to the hardware context. Second, we propose a novel algorithm based on multiresolutional spectral analysis of adjacency matrices. Third, in three extensively evaluated case studies, namely (1) gate-level netlist reverse engineering, (2) hardware Trojan detection, and (3) assessment of hardware obfuscation, we demonstrate the practical nature of graph similarity algorithms.

The threat of inserting hardware Trojans during the design, production, or in-field poses a danger for integrated circuits in real-world applications. A particular critical case of hardware Trojans is the malicious manipulation of third-party FPGA configurations. In addition to attack vectors during the design process, FPGAs can be infiltrated in a non-invasive manner after shipment through alterations of the bitstream. First, we present an improved methodology for bitstream file format reversing. Second, we introduce a novel idea for Trojan insertion.

In contrast to software reverse engineering, there are hardly any tools available that support hardware reversing. Therefore, the reversing process is conducted by human analysts combining several complex semi-automated steps. However, countermeasures against reversing are evaluated solely against mathematical models. Our research goal is the establishment of cognitive obfuscation based on the exploration of underlying psychological processes. We aim to identify problems which are hard to solve for human analysts and derive novel quantification metrics, thus enabling stronger obfuscation techniques.

Since hardware oftentimes serves as the root of trust in our modern interconnected world, malicious hardware manipulations constitute a ubiquitous threat in the context of the Internet of Things (IoT). Hardware reverse engineering is a prevalent technique to detect such manipulations.

Hardware manipulations pose a serious threat to numerous systems, ranging from a myriad of smart-X devices to military systems. In many attack scenarios an adversary merely has access to the low-level, potentially obfuscated gate-level netlist. In general, the attacker possesses minimal information and faces the costly and time-consuming task of reverse engineering the design to identify security-critical circuitry, followed by the insertion of a meaningful hardware Trojan. These challenges have been considered only in passing by the research community. The contribution of this work is threefold: First, we present HAL, a comprehensive reverse engineering and manipulation framework for gate-level netlists. HAL allows automating defensive design analysis (e.g., including arbitrary Trojan detection algorithms with minimal effort) as well as offensive reverse engineering and targeted logic insertion. Second, we present a novel static analysis Trojan detection technique ANGEL which considerably reduces the false-positive detection rate of the detection technique FANCI. Furthermore, we demonstrate that ANGEL is capable of automatically detecting Trojans obfuscated with DeTrust. Third, we demonstrate how a malicious party can semi-automatically inject hardware Trojans into third-party designs. We present reverse engineering algorithms to disarm and trick cryptographic self-tests, and subtly leak cryptographic keys without any a priori knowledge of the design's internal workings.