Technique for detecting hardware-based Trojans using a convolutional neural network

The hardware Trojan, also known as HT, has emerged as a danger to the integrated circuit (IC) sector and the supply chain, leading to the creation of a plethora of Trojan detection strategies. The detection of HT is very necessary to ensure both the chip's functionality and its safety. This article discusses a recently discovered risk to integrated circuits (ICs) safety. Using a deep convolutional neural network, the authors of this research offer a novel partial RE-based HT detection algorithm. This method can identify Trojan horses in IC layout photos (DCNN). The suggested DCNN model is made up of many convolutional and pooling layers that are stacked on top of one another. By giving proof of concept implementation of the various approaches to FPGAs, we demonstrate the practicability of the presented strategies by demonstrating how they may be implemented.