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87owo authored and GitHub committed 2026-05-03 03:20:31 +08:00
1 parent 43be2987f5
commit 5fd4efd920
2 files changed
+38 -12
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Engine/Properties/train_server.py
+19 -6
View File
@@ -14,7 +14,7 @@ DB_PATH = "pe_features.db"
MODEL_FILE = "model.txt"
ONNX_FILE = "Pefile_General_S1.onnx"
FEATURE_FILE = "features.json"
TEST_SIZE = 0.0001
TEST_SIZE = 0.1
RANDOM_SEED = 42
LGBM_PARAMS = {
@@ -31,8 +31,8 @@ LGBM_PARAMS = {
'n_jobs': -1,
'max_depth': -1,
'min_data_in_leaf': 10,
'lambda_l1': 0.1,
'lambda_l2': 0.1,
'lambda_l1': 0.05,
'lambda_l2': 0.05,
}
####################################################################################################
@@ -170,11 +170,24 @@ def evaluate_and_save(model, X_test, y_test):
acc = accuracy_score(y_test, y_pred)
auc = roc_auc_score(y_test, y_prob)
tp = np.sum((y_test == 1) & (y_pred == 1))
fn = np.sum((y_test == 1) & (y_pred == 0))
tn = np.sum((y_test == 0) & (y_pred == 0))
fp = np.sum((y_test == 0) & (y_pred == 1))
total_malware = tp + fn
total_safe = tn + fp
detection_rate = (tp / total_malware * 100) if total_malware > 0 else 0.0
fpr = (fp / total_safe * 100) if total_safe > 0 else 0.0
print(f"Accuracy : {acc:.4f}")
print(f"ROC AUC : {auc:.4f}")
print("\n" + classification_report(y_test, y_pred, zero_division=0))
print(f"ROC AUC : {auc:.4f}\n")
print(f"True Positive (Class 1) : {detection_rate:.3f}% ({tp}/{total_malware})")
print(f"False Positive (Class 0) : {fpr:.3f}% ({fp}/{total_safe})")
print("---------------------------------------------------------\n")
print("\n---------------------------------------------------------\n")
print("[*] Top 20 Features (Importance by Gain):")
feature_names = model.feature_name()
Engine/Properties/train_user.py
+19 -6
View File
@@ -14,7 +14,7 @@ DB_PATH = "pe_features.db"
MODEL_FILE = "model.txt"
ONNX_FILE = "Pefile_General_T1.onnx"
FEATURE_FILE = "features.json"
TEST_SIZE = 0.0001
TEST_SIZE = 0.1
RANDOM_SEED = 42
LGBM_PARAMS = {
@@ -31,8 +31,8 @@ LGBM_PARAMS = {
'n_jobs': -1,
'max_depth': -1,
'min_data_in_leaf': 10,
'lambda_l1': 0.1,
'lambda_l2': 0.1,
'lambda_l1': 0.05,
'lambda_l2': 0.05,
}
####################################################################################################
@@ -170,11 +170,24 @@ def evaluate_and_save(model, X_test, y_test):
acc = accuracy_score(y_test, y_pred)
auc = roc_auc_score(y_test, y_prob)
tp = np.sum((y_test == 1) & (y_pred == 1))
fn = np.sum((y_test == 1) & (y_pred == 0))
tn = np.sum((y_test == 0) & (y_pred == 0))
fp = np.sum((y_test == 0) & (y_pred == 1))
total_malware = tp + fn
total_safe = tn + fp
detection_rate = (tp / total_malware * 100) if total_malware > 0 else 0.0
fpr = (fp / total_safe * 100) if total_safe > 0 else 0.0
print(f"Accuracy : {acc:.4f}")
print(f"ROC AUC : {auc:.4f}")
print("\n" + classification_report(y_test, y_pred, zero_division=0))
print(f"ROC AUC : {auc:.4f}\n")
print(f"True Positive (Class 1) : {detection_rate:.3f}% ({tp}/{total_malware})")
print(f"False Positive (Class 0) : {fpr:.3f}% ({fp}/{total_safe})")
print("---------------------------------------------------------\n")
print("\n---------------------------------------------------------\n")
print("[*] Top 20 Features (Importance by Gain):")
feature_names = model.feature_name()