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- Mireu-Lab/NSL-KDD
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metrics:
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- accuracy
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pipeline_tag: text-
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tags:
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- intrusion
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---
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PCF_ID: A Novel Prompt Cast Framework for Intrusion Detection
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[](https://huggingface.co/
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[](https://www.python.org/)
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[](https://huggingface.co/PHZane/PCF_ID-0.5B)
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[](https://www.python.org/)
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[](https://opensource.org/licenses/MIT)
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## 📌 Model Overview
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**PCF_ID-0.5B** is a fine-tuned version of **Qwen2.5-0.5B**, adapted for network intrusion detection using the **Prompt Cast Framework (PCF-ID)**. By transforming structured tabular records into semantically rich **Question–Reasoning–Answer (Q-R-A)** prompts, PCF_ID enables large language models to effectively reason over cybersecurity data.
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Key results on the **NSL-KDD** benchmark:
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- **Accuracy**: >94%
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- **Precision**: >0.97
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- **Recall**: >0.95
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- **F1 Score**: >0.96
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PCF_ID-0.5B significantly outperforms:
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- Raw LLMs (e.g., vanilla Qwen2.5-0.5B),
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- Traditional machine learning models (e.g., Random Forest, SVM),
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- Advanced graph neural networks (GNNs).
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## 💻 Code & Reproduction
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The full framework, preprocessing pipeline, and evaluation scripts are available at:
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🔗 [https://github.com/Zaneph1/PCF_ID](https://github.com/Zaneph1/PCF_ID)
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