Abstract
Side-channel attacks (SCAs) are powerful noninvasive attacks that can be used for leaking the secret key of integrated circuits (ICs). Numerous countermeasures were proposed to elevate the security level of ICs against SCAs. Unfortunately, it is quite inconvenient to predict the security levels of these countermeasures since no solid mathematical model exists in the literature. In this paper, neural network (NN)-based entropy is proposed to model the resilience of a system against SCAs. The NN-based entropy model well links the side-channel leakages and probabilities with the neurons and weights of NNs, respectively. In such a circumstance, the NN-based entropy can be used for modeling the robustness of countermeasures since a one-to-one relationship is established between the NN-based entropy and the measurement-to-disclose (MTD) enhancement ratio related with the countermeasures. As demonstrated in the result, the proposed NN-based entropy metric shows 100% consistency with the MTD enhancement ratio if multiple SCA countermeasures are employed into a system.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
