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
File size: 507 Bytes
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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 |