FPGA Centric Attention Based Deep Learning Network Evoked Chaotic Encryption to Mitigate Side Channel Attacks

Abstract

Security concerns are growing, especially in applications where sensitive information is sent (such as health care devices, credit cards, and even Internet of Things (IoT) nodes), even while these developments bring in a modern revolution. This is due to the constant scaling of technologies, which has now been incredibly useful for delivering the new trend of development in the numerous domains such as communications, semiconductors, health care, and automation. Even when cutting-edge standard encryption algorithms are used in smart devices, the principal threats, such as Side-Channel Attacks (SCA) and Correlation Power Analysis (CPA), increase the susceptibility of encrypted cipher text to attacks. In order to defend against side channel assaults, the usefulness of deep learning algorithms combined with chaotic principles is examined in this paper. Scroll Mapping (SM) and Attention-Evoked Long Short-Term Memory (AE-LSTM) are used in the proposed study to effectively safeguard sensitive data while consuming little power. The most promising advancements in terms of all security metrics and hardware capabilities for obtaining high-speed performances are shown by experimental findings on an FPGA implementation of a suggested framework.