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@@ -4,27 +4,39 @@ datasets:
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  - Mireu-Lab/NSL-KDD
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  metrics:
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  - accuracy
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- base_model:
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- - Qwen/Qwen2.5-0.5B
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- pipeline_tag: text-generation
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  tags:
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- - intrusion detection
 
 
 
 
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  ---
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- PCF_ID: A Novel Prompt Cast Framework for Intrusion Detection
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- [![Hugging Face Model](https://img.shields.io/badge/Hugging%20Face-Model-blue)](https://huggingface.co/your_model_name_here)
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  [![Python 3.8+](https://img.shields.io/badge/Python-3.8%2B-blue)](https://www.python.org/)
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- [![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
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- 📌 模型概述
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- PCF_ID-0.5B ,通过 PCF-ID 框架针对结构化入侵检测数据进行了微调,实现了从原始 LLMs 到安全领域应用的重大跃迁:
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- 准确率:高达 94%以上
 
 
 
 
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- F1 分数:达到 0.94以上
 
 
 
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- 在标准入侵检测NSL-KDD基准上显著优于大模型,传统机器学习方法和先进的图神经网络。
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- 代码提供在:https://github.com/Zaneph1/PCF_ID
 
 
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  - Mireu-Lab/NSL-KDD
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  metrics:
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  - accuracy
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+ - f1
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+ base_model: Qwen/Qwen2.5-0.5B
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+ pipeline_tag: text-classification
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  tags:
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+ - intrusion-detection
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+ - cybersecurity
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+ - llm
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+ - qwen
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+ - tabular-reasoning
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  ---
 
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+ # PCF_ID: A Novel Prompt Cast Framework for Intrusion Detection
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+ [![Hugging Face Model](https://img.shields.io/badge/Hugging%20Face-Model-blue)](https://huggingface.co/PHZane/PCF_ID-0.5B)
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  [![Python 3.8+](https://img.shields.io/badge/Python-3.8%2B-blue)](https://www.python.org/)
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+ [![License](https://img.shields.io/badge/License-MIT-green.svg)](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)