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.