Real-Time Switched Capacitor Based Power Side-Channel Attack Detection

Side-channel attack (SCA) is regarded as a sig- nificant risk to the hardware implementation of cryptographic systems. Side-channel information, such as timing, power, and electromagnetic radiation, is leaked through the system and can be exploited for secret key extraction. The work proposes a real- time and compatible detection method for power SCAs. The technique makes use of a switched capacitor DC-DC (SC-DCDC) converter along with a lightweight artificial intelligence engine for power SCA detection. The proposed system, referred to as EoH, has the ability to perform dynamic voltage scaling and learn the behaviors of the cryptographic system to identify any potential attacks. The switching activities of the SC-DCDC converter can be viewed as a measurement of the cryptographic function. Thus, the recurrent neural network was chosen as it best processes timeseries data. The technique is system-specific, meaning that during the enrollment phase, the normal operation of the system is learned. The technique can also be expanded to include other types of SCA and is not limited to power.