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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.
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, countermeasure
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,
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To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior res
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