Visible to the public A Neural Network Trojan Detection Method Based on Particle Swarm Optimization

TitleA Neural Network Trojan Detection Method Based on Particle Swarm Optimization
Publication TypeConference Paper
Year of Publication2018
AuthorsWang, C., Zhao, S., Wang, X., Luo, M., Yang, M.
Conference Name2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT)
Date PublishedNov. 2018
ISBN Number978-1-5386-4441-6
KeywordsArtificial neural networks, cryptography, Hardware, hardware Trojan detection, HSPICE power consumption simulation, Integrated circuit modeling, intelligent neural networks algorithm, invasive software, malicious modifications, neural nets, neural network Trojan detection, particle swarm optimisation, particle swarm optimization, power aware computing, power consumption, power consumption data, Power demand, principal component analysis, Principal Component Analysis algorithm, PSO NN method, pubcrawl, RTL circuits, Side Channel Analysis technology, trojan horse detection, Trojan horses

Hardware Trojans (HTs) are malicious modifications of the original circuits intended to leak information or cause malfunction. Based on the Side Channel Analysis (SCA) technology, a set of hardware Trojan detection platform is designed for RTL circuits on the basis of HSPICE power consumption simulation. Principal Component Analysis (PCA) algorithm is used to reduce the dimension of power consumption data. An intelligent neural networks (NN) algorithm based on Particle Swarm Optimization (PSO) is introduced to achieve HTs recognition. Experimental results show that the detection accuracy of PSO NN method is much better than traditional BP NN method.

Citation Keywang_neural_2018