Instructions to use Canstralian/CyberAttackDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Canstralian/CyberAttackDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Canstralian/CyberAttackDetection")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("Canstralian/CyberAttackDetection", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Create dataset.json
Browse files- dataset.json +10 -0
dataset.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"text": [
|
| 3 |
+
"A vulnerability was discovered in the server software.",
|
| 4 |
+
"An SQL injection attack was attempted on the web server.",
|
| 5 |
+
"The system is vulnerable to buffer overflow exploitation.",
|
| 6 |
+
"Regular server maintenance performed successfully.",
|
| 7 |
+
"The user input was sanitized to prevent command injection."
|
| 8 |
+
],
|
| 9 |
+
"label": [1, 1, 1, 0, 0]
|
| 10 |
+
}
|