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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| pipeline_tag: text-classification | |
| tags: | |
| - malware | |
| - syscalls | |
| - bigbird | |
| BigBird model trained on syscalls for malware detection. | |
| It has five labels: Normal and four types of malware: Bashlite, TheTick, Bdvl, RansomwarePoC | |
| Dataset: https://ieee-dataport.org/documents/malwspecsys-dataset-containing-syscalls-iot-spectrum-sensor-affected-heterogeneous-malware | |
| Paper: https://arxiv.org/abs/2405.09318 | |
| Published at IEEE MILCOM 2024 | |
| Uploaded by https://huggingface.co/pedromiguelsanchez |