Deep Learning-Based Side-Channel Attacks on Secure and Conventional Cryptographic Circuits Using FinFET and TFET Technologies

Electronic devices are now ubiquitous across both professional and personal domains, often containing sensitive information that should remain undisclosed to untrustworthy third parties. Consequently, there is an increased demand for effective security measures to prevent the leakage of confidential data. While some devices utilize mathematically secure algorithms to safeguard sensitive information, there remains a vulnerability to informational leaks through Side-Channel Attacks (SCAs) targeting hardware platforms. Non-profiled SCAs, including Correlation Power Analysis (CPA), are particularly practical since they require access only to the target device. In this study, we propose and investigate the use of Deep Learning (DL) techniques to enhance the effectiveness of non-profiled SCAs through an optimized Deep Learning Power Analysis (DLPA) algorithm. Optimized DLPA attacks are implemented using Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) models, and are applied to the PRIDE SBox-4 block across conventional CMOS-style circuits and secure Sense Amplifier-Based Logic (SABL) Dual Precharge Logic (DPL) structure circuits. Both FinFET and TFET device technologies are evaluated. The experimental results show that the optimized DLPA approach consistently outperforms traditional CPA attacks. The optimized DLPA method succeeds even against TFET-based SABL-DPL circuits, which are resistant to conventional techniques. These findings demonstrate the increased threat posed by DL-based SCAs and highlight the need for evaluating hardware security against advanced machine learning-based methods.