The invention belongs to the technical field of hardware security, and discloses a 
machine learning 
Trojan horse detection method based on structural 
feature screening and load expansion, which comprises the following steps: firstly, converting a 
netlist of a circuit into a quantifiable 
mathematical model, and performing 
feature extraction through a mathematical method based on the model; then, in combination with 
hardware Trojan trigger structure characteristics, screening nodes to obtain a more balanced 
data set, and carrying out Trojan detection in combination with a 
machine learning classification method; and finally, according to the structural characteristics of the 
hardware Trojan load, carrying out backward expansion on Trojan nodes so as to obtain a complete 
hardware Trojan circuit. According to the method, the structural features of the 
Trojan horse with low trigger probability and the circuit static features used by 
machine learning are creatively combined, the 
data set of 
machine learning is preliminarily screened, the 
data set for training is balanced, the efficiency and accuracy of 
machine learning are effectively improved, a new thought is provided for subsequent related research, and the detection effects of most 
hardware Trojan horse detection methods are improved.