bert-base-cybersecurity

1. Model Details

Model description
"bert-base-cybersecurity" is a transformer model adapted for cybersecurity text classification tasks (e.g., threat detection, incident reports, malicious vs benign content).

• Model type: fine-tuned lightweight BERT variant
• Languages: English & Indonesia
• Finetuned from: bert-base-cased
• Status: Early version — trained on 0.00% of planned data.

Model sources

• Base model: google-bert/bert-base-cased
• Data: Cybersecurity Data

2. Uses

Direct use

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.

Downstream use

• Embedding extraction for clustering or anomaly detection in security logs.
• As part of a pipeline for phishing detection, malicious email filtering, incident triage.
• As a feature extractor feeding a downstream system (e.g., alert-generation, SOC dashboard).

Out-of-scope use

• Not meant for high-stakes automated blocking decisions without human review.
• Not optimized for languages other than English and Indonesian.
• Not tested for non-cybersecurity domains or out-of-distribution data.

3. Bias, Risks, and Limitations

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).

• 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.
• Should not be used as sole authority for incident decisions; only as an aid to human analysts.

4. How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-base-cybersecurity")
model = AutoModelForSequenceClassification.from_pretrained("codechrl/bert-base-cybersecurity")

inputs = tokenizer("The server logged an unusual outbound connection to 123.123.123.123", 
                   return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax(dim=-1).item()

5. Training Details

• Trained records: 1 / 237,628 (0.00%)
• Learning rate: 5e-05
• Epochs: 3
• Batch size: 1
• Max sequence length: 512