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Code Vulnerability Detection Based on Graph Neural Network

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16 juin 2025
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Figure 1.

The Process of Code Standardization
The Process of Code Standardization

Figure 2.

The Process of Generating the PDG
The Process of Generating the PDG

Figure 3.

The Process of Slicing PDG
The Process of Slicing PDG

Figure 4.

The Process of Extracting Features from the Slice Graph
The Process of Extracting Features from the Slice Graph

Figure 5.

Vulnerability detection model architecture
Vulnerability detection model architecture

Figure 6.

Results of the Model Training in the Proposed Network
Results of the Model Training in the Proposed Network

Figure 7.

Ablation experiment
Ablation experiment

Figure 8.

Ablation experiment
Ablation experiment

Figure 9.

loss comparison
loss comparison

Figure 10.

Performance comparison of different models under evaluation
Performance comparison of different models under evaluation

TABLE TYPE STYLES

Parameter Value
Loss Function CrossEntropyLoss
Optimization Algorithm Adam
Learning Rate 0.0001
Weight Decay 0.001
Batch Size 16
Training Epochs 500
Max Steps 10000
Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Informatique, Informatique, autres