CGAN-based Automated Fault Injection

Fault injection is a major threat to electronic devices. Consequently, developers or independent test labs spend significant time and effort in identifying fault injection vulnerabilities. While there are proposed methods to automate fault injection vulnerability identification, their performance depend on observing many successful glitches first to generate more of them. This takes a considerable amount of time and it is common for fault injection campaigns to produce no or very few successful glitches initially. To address this issue, we propose a conditional generative adversarial network (CGAN)-based automated fault injection method. Our method finds promising regions in the parameter space even without observing successful glitches, as well as adjusts itself after observing them. Experimental results show that our method proposes at least 1.3 times more successful glitch parameters than the state of the art in both voltage and electromagnetic (EM)-based fault injection.