Integrated Circuits (ICs) are omnipresent, yet their globalized manufacturing process remains vulnerable to supply chain threats. Hardware Reverse Engineering (HRE) is essential for detecting such threats and re-establishing trust; however domain experts remain scarce due to a lack of educational programs. To contribute educational insights in this critical and rapidly evolving technology domain, we present our HRE course focusing on digital circuit analysis and digital circuit extraction from ICs. The course targets junior-level undergraduates at a major European research university. The curriculum has been refined over nine iterations (2017–2025), with several alumni subsequently pursuing careers in the HRE field. By reflecting on the evolution of the course organization, content, and assignments, we derive key lessons learned. We further distill these insights into actionable design priorities for educators developing courses in rapidly evolving technological domains, emphasizing iterative growth and sustainable workload management for both students and instructors.

Hardware Reverse Engineering (HRE) is a technique for analyzing integrated circuits.Experts employ HRE for security-critical tasks, like detecting Trojans or intellectual property violations, relying not only on their experience and customized tools but also on their cognitive abilities.In this work, we introduce ReverSim, a software environment that models key HRE subprocesses and integrates standardized cognitive tests.ReverSim enables quantitative studies with easier-to-recruit non-experts to uncover cognitive factors relevant to HRE.We empirically evaluated ReverSim in three studies.Semi-structured interviews with 14 HRE professionals confirmed its comparability to real-world HRE processes.Two online user studies with 170 novices and intermediates revealed effective differentiation of participant performance across a spectrum of difficulties, and correlations between participants' cognitive processing speed and task performance.ReverSim is available as open-source software, providing a robust platform for controlled experiments to assess cognitive processes in HRE, potentially opening new avenues for hardware protection.

Trust in digital systems depends on secure hardware, often assured through HRE. This work develops methods for investigating human problem-solving processes in HRE, an underexplored yet critical aspect. Since reverse engineers rely heavily on visual information, eye tracking holds promise for studying their cognitive processes. To gain further insights, we additionally employ verbal thought protocols during and immediately after HRE tasks: Concurrent and Retrospective Think Aloud. We evaluate the combination of eye tracking and Think Aloud with 41 participants in an HRE simulation. Eye tracking accurately identifies fixations on individual circuit elements and highlights critical components. Based on two use cases, we demonstrate that eye tracking and Think Aloud can complement each other to improve data quality. Our methodological insights can inform future studies in HRE, a specific setting of human-computer interaction, and in other problem-solving settings involving misleading or missing information.

Understanding of microchips, known as Hardware Reverse Engineering (HRE), is driven by analysts’ problem solving. This work sheds light on these hitherto poorly understood problem-solving processes. We propose a methodology addressing the problem of HRE experts being unavailable for research. We developed a training enabling students to acquire intermediate levels of HRE expertise. Besides one expert, we recruited eight top-performing students from this training for our exploratory study. All participants completed a realistic HRE task involving the removal of a copyright protection mechanism from a hardware circuit. We analyzed 2,445 log entries by applying an iterative open coding and developed a detailed hierarchical problem-solving model. Our exploration yielded insights into problem-solving strategies and revealed that two intermediates solved the task with a comparable solution time to the expert. We discuss that HRE problem solving may be a function of both expertise and cognitive abilities, and outline ideas for novel countermeasures.

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.

This full research paper focuses on skill acquisition in Hardware Reverse Engineering (HRE) - an important field of cyber security. HRE is a prevalent technique routinely employed by security engineers (i) to detect malicious hardware manipulations, (ii) to conduct VLSI failure analysis, (iii) to identify IP infringements, and (iv) to perform competitive analyses. Even though the scientific community and industry have a high demand for HRE experts, there is a lack of educational courses. We developed a university-level HRE course based on general cognitive psychological research on skill acquisition, as research on the acquisition of HRE skills is lacking thus far. To investigate how novices acquire HRE skills in our course, we conducted two studies with students on different levels of prior knowledge. Our results show that cognitive factors (e.g., working memory), and prior experiences (e.g., in symmetric cryptography) influence the acquisition of HRE skills. We conclude by discussing implications for future HRE courses and by outlining ideas for future research that would lead to a more comprehensive understanding of skill acquisition in this important field of cyber security.