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--- |
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language: |
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- en |
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- id |
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tags: |
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- bert |
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- text-classification |
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- token-classification |
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- cybersecurity |
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- fill-mask |
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- named-entity-recognition |
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base_model: google-bert/bert-base-cased |
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library_name: transformers |
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--- |
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# bert-base-cybersecurity |
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## 1. Model Details |
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**Model description** |
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"bert-base-cybersecurity" is a transformer model adapted for cybersecurity text classification tasks (e.g., threat detection, incident reports, malicious vs benign content). |
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- Model type: fine-tuned lightweight BERT variant |
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- Languages: English & Indonesia |
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- Finetuned from: `bert-base-cased` |
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- Status: **Early version** — trained on **0.00%** of planned data. |
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**Model sources** |
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- Base model: [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) |
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- Data: Cybersecurity Data |
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## 2. Uses |
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### Direct use |
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You can use this model to classify cybersecurity-related text — for example, whether a given message, report or log entry indicates malicious intent, abnormal behaviour, or threat presence. |
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### Downstream use |
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- Embedding extraction for clustering or anomaly detection in security logs. |
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- As part of a pipeline for phishing detection, malicious email filtering, incident triage. |
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- As a feature extractor feeding a downstream system (e.g., alert-generation, SOC dashboard). |
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### Out-of-scope use |
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- Not meant for high-stakes automated blocking decisions without human review. |
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- Not optimized for languages other than English and Indonesian. |
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- Not tested for non-cybersecurity domains or out-of-distribution data. |
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## 3. Bias, Risks, and Limitations |
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Because the model is based on a small subset (0.00%) of planned data, performance is preliminary and may degrade on unseen or specialized domains (industrial control, IoT logs, foreign language). |
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- Inherits any biases present in the base model (`google-bert/bert-base-cased`) and in the fine-tuning data — e.g., over-representation of certain threat types, vendor or tooling-specific vocabulary. |
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- Should not be used as sole authority for incident decisions; only as an aid to human analysts. |
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## 4. How to Get Started with the Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-base-cybersecurity") |
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model = AutoModelForSequenceClassification.from_pretrained("codechrl/bert-base-cybersecurity") |
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inputs = tokenizer("The server logged an unusual outbound connection to 123.123.123.123", |
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return_tensors="pt", truncation=True, padding=True) |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class = logits.argmax(dim=-1).item() |
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``` |
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## 5. Training Details |
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- **Trained records**: 1 / 237,628 (0.00%) |
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- **Learning rate**: 5e-05 |
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- **Epochs**: 3 |
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- **Batch size**: 1 |
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- **Max sequence length**: 512 |
