NLP model and tech for cyber security task
weak password detection with machine learning
weak-password/password-strength detection with machine learning; 弱密码检测;密码强度检测
precision recall f1-score support
0 0.94406 0.83240 0.88472 8920
1 0.96327 0.98971 0.97631 49652
2 0.99035 0.95400 0.97184 8392
accuracy 0.96428 66964
macro avg 0.96589 0.92537 0.94429 66964
weighted avg 0.96410 0.96428 0.96355 66964
xss injection detection with machine learning
Precision score is : 0.9764296754250387
Recall score is : 0.9830772223302859
Precision score is : 0.9948463825569871
Recall score is : 0.9762692083252286
Precision score is : 0.9980311084859225
Recall score is : 0.9869548286604362
malicious url detection with machine learning
Accuracy Score is: 0.8655441478439425
Precision Score is : 0.8579050828418984
Recall Score is : 0.8767578205075642
F1 Score: 0.8672290036092299
AUC Score: 0.8655252346603806
Accuracy Score is: 0.8379671457905544
Precision Score is : 0.8431494883953082
Recall Score is : 0.831085236357673
F1 Score: 0.8370738958974254
AUC Score: 0.8379787529437384
Accuracy Score is: 0.9242505133470226
Precision Score is : 0.9288969917958068
Recall Score is : 0.9191095076052642
F1 Score: 0.92397733127254
AUC Score: 0.9242591842604873
phishing url detection with machine learning
accuracy: 0.9982
Model Accuracy: 99.82%
precision recall f1-score support
0 0.99790 0.99895 0.99843 1904
1 0.99866 0.99732 0.99799 1495
accuracy 0.99823 3399
macro avg 0.99828 0.99814 0.99821 3399
weighted avg 0.99824 0.99823 0.99823 3399