Nils Albartus

Sebastian Wallat, Nils Albartus, Steffen Becker et al.

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. Over the last years, an active research community has significantly advanced the field of hardware reverse engineering. Notably, many open research questions regarding the extraction of functionally correct netlists from Field Programmable Gate Arrays (FPGAs) or Application Specific Integrated Circuits (ASICs) have been tackled. In order to facilitate further analysis of recovered netlists, a software framework is required, serving as the foundation for specialized algorithms. Currently, no such framework is publicly available. Therefore, we provide the first open-source gate-library agnostic framework for gate-level netlist analysis. In this positional paper, we demonstrate the workflow of our modular framework HAL on the basis of two case studies and provide profound insights on its technical foundations.

Steffen Becker, Carina Wiesen, Nils Albartus et al.

Understanding the internals of Integrated Circuits (ICs), referred to as Hardware Reverse Engineering (HRE), is of interest to both legitimate and malicious parties. HRE is a complex process in which semi-automated steps are interwoven with human sense-making processes. Currently, little is known about the technical and cognitive processes which determine the success of HRE. This paper performs an initial investigation on how reverse engineers solve problems, how manual and automated analysis methods interact, and which cognitive factors play a role. We present the results of an exploratory behavioral study with eight participants that was conducted after they had completed a 14-week training. We explored the validity of our findings by comparing them with the behavior (strategies applied and solution time) of an HRE expert. The participants were observed while solving a realistic HRE task. We tested cognitive abilities of our participants and collected large sets of behavioral data from log files. By comparing the least and most efficient reverse engineers, we were able to observe successful strategies. Moreover, our analyses suggest a phase model for reverse engineering, consisting of three phases. Our descriptive results further indicate that the cognitive factor Working Memory (WM) might play a role in efficiently solving HRE problems. Our exploratory study builds the foundation for future research in this topic and outlines ideas for designing cognitively difficult countermeasures ("cognitive obfuscation") against HRE.

Carina Wiesen, Nils Albartus, Max Hoffmann et al.

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.

Carina Wiesen, Steffen Becker, Marc Fyrbiak et al.

Since underlying hardware components form the basis of trust in virtually any computing system, security failures in hardware pose a devastating threat to our daily lives. Hardware reverse engineering is commonly employed by security engineers in order to identify security vulnerabilities, to detect IP violations, or to conduct very-large-scale integration (VLSI) failure analysis. Even though industry and the scientific community demand experts with expertise in hardware reverse engineering, there is a lack of educational offerings, and existing training is almost entirely unstructured and on the job. To the best of our knowledge, we have developed the first course to systematically teach students hardware reverse engineering based on insights from the fields of educational research, cognitive science, and hardware security. The contribution of our work is threefold: (1) we propose underlying educational guidelines for practice-oriented courses which teach hardware reverse engineering; (2) we develop such a lab course with a special focus on gate-level netlist reverse engineering and provide the required tools to support it; (3) we conduct an educational evaluation of our pilot course. Based on our results, we provide valuable insights on the structure and content necessary to design and teach future courses on hardware reverse engineering.