نوع مقاله : مقاله پژوهشی
نویسنده
گروه مهندسی برق، الکترونیک و مخابرات، دانشکده مهندسی، دانشگاه کردستان، سنندج، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسنده [English]
The vulnerability of digital integrated circuits against the Hardware Trojans (HT) has increased in recent decades due to the implementation of more complex systems on them. HTs could become a source of errors or apply to steel important information embedded in the implemented circuits. So, analyzing the vulnerability of digital integrated circuits in the early stages of production is of great merit. In this paper, a novel vulnerability classification method is introduced based on the deep convolutional neural networks (CNN) wherein five major effective features of vulnerability assessment are utilized (white space distribution, unutilized routing resources, signal activity of circuit nodes, delay of the circuit paths and, controllability of circuit nodes). In the proposed framework, first of all, a dataset containing 10000 images is generated using various digital circuit implementations. Then, a deep CNN is trained using the generated dataset meanwhile the most appropriate CNN’s hyperparameters are achieved using a greedy optimization method. The simulation results reveal 92% accuracy of vulnerability classification which shows a 17% improvement in comparison with the best linear classifier and analytical methods.
کلیدواژهها [English]