基于图Transformer的门级硬件木马检测方法OA

Graph neural networks can effectively learn and detect maliciously implanted hardware trojans(HT)in gate-level netlists.However,existing methods were usually trained on isomorphic and same-process circuits,which resulted in the generalization ability of the models being limited by the process and structural characteristics of the training set,making it difficult to cope with the graph structure distribution shift caused by the differences between the target circuit and the training set.To address this issue,a gate-level HT detection method based on graph trans-former was proposed.Firstly,the gate-level netlist was converted into a general graph structure,and a multi-dimensional feature extraction module was designed to capture the intrinsic attributes and topological information of nodes.Secondly,a three-layer stacked graph Transformer was adopted for feature encoding to capture the global dependencies between nodes,and multi-level features were fused to enhance the expression ability.Finally,a dual-task classifier with a gradient reversal layer was introduced to suppress the domain shift caused by circuit heteroge-neity.Experiments at the Trust-Hub public library showed that the proposed method achieved a recall rate of 95.2%on the synopsys 90 nm general library SAED,and its F1-score was improved compared with existing mainstream methods.On the large-scale dataset based on TRIT,it achieved an average recall rate of 87.9%and an average F1-score of 85.0%.This method effectively improves the robustness of HT detection models in cross-process or hetero-geneous netlists.