LOCK&ROLL: Deep-Learning Power Side-Channel Attack Mitigation using Emerging Reconfigurable Devices and Logic Locking

Abstract

The security and trustworthiness of ICs are exacerbated by the modern globalized semiconductor business model. This model involves many steps performed at multiple locations by different providers and integrates various Intellectual Properties (IPs) from several vendors for faster time-to-market and cheaper fabrication costs. Many existing works have focused on mitigating the well-known SAT attack and its derivatives. Power Side-Channel Attacks (PSCAs) can retrieve the sensitive contents of the IP and can be leveraged to find the key to unlock the obfuscated circuit without simulating powerful SAT attacks. To mitigate P-SCA and SAT-attack together, we propose a multi-layer defense mechanism called LOCK&ROLL: Deep-Learning Power Side-Channel Attack Mitigation using Emerging Reconfigurable Devices and Logic Locking. LOCK&ROLL utilizes our proposed Magnetic Random-Access Memory (MRAM)-based Look Up Table called Symmetrical MRAM-LUT (SyM-LUT). Our simulation results using 45nm technology demonstrate that the SyM-LUT incurs a small overhead compared to traditional Static Random Access Memory LUT (SRAM-LUT). Additionally, SyM-LUT has a standby energy consumption of 20aJ while consuming 33fJ and 4.6fJ for write and read operations, respectively. LOCK&ROLL is resilient against various attacks such as SAT-attacks, removal attack, scan and shift attacks, and P-SCA.