Instructions to use CyberDataLab/BigBird_Syscall_Malware with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CyberDataLab/BigBird_Syscall_Malware with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CyberDataLab/BigBird_Syscall_Malware")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CyberDataLab/BigBird_Syscall_Malware") model = AutoModelForSequenceClassification.from_pretrained("CyberDataLab/BigBird_Syscall_Malware") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -9,4 +9,6 @@ tags:
|
|
| 9 |
|
| 10 |
BigBird model trained on syscalls for malware detection.
|
| 11 |
|
|
|
|
|
|
|
| 12 |
Dataset: https://ieee-dataport.org/documents/malwspecsys-dataset-containing-syscalls-iot-spectrum-sensor-affected-heterogeneous-malware
|
|
|
|
| 9 |
|
| 10 |
BigBird model trained on syscalls for malware detection.
|
| 11 |
|
| 12 |
+
It has five labels: Normal and four types of malware: Bashlite, TheTick, Bdvl, RansomwarePoC
|
| 13 |
+
|
| 14 |
Dataset: https://ieee-dataport.org/documents/malwspecsys-dataset-containing-syscalls-iot-spectrum-sensor-affected-heterogeneous-malware
|