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README.md

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-# Model Card for AI-Driven Exploit Generation
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+
+# Model Card for Canstralian/CyberAttackDetection
+
+This model card provides details for the Canstralian/CyberAttackDetection model, fine-tuned from 'WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B.' The model is licensed under the MIT license and is designed for detecting and analyzing potential cyberattacks, primarily in the context of network security.
 
 ## Model Details
 
 ### Model Description
-The **AI-Driven Exploit Generation** model is designed to assist cybersecurity researchers and penetration testers in simulating exploit generation and analysis. The model leverages state-of-the-art natural language processing (NLP) techniques to understand vulnerabilities and create theoretical exploit scenarios in a controlled and ethical environment. It aids in improving vulnerability management by providing insights into potential exploit paths, fostering proactive defense strategies.
 
-- **Developed by:** Canstralian  
-- **Funded by:** Self-funded  
-- **Shared by:** Canstralian  
-- **Model type:** Transformer-based language model for cybersecurity tasks  
-- **Language(s) (NLP):** English  
-- **License:** MIT License  
-- **Finetuned from model:** [Base model or framework, e.g., GPT-based or similar]
+The Canstralian/CyberAttackDetection model is a machine learning-based cybersecurity tool developed for identifying and analyzing cyberattacks in real-time. Fine-tuned on datasets containing CVE (Common Vulnerabilities and Exposures) data and other OSINT resources, the model leverages advanced natural language processing capabilities to enhance threat intelligence and detection.
+
+- **Developed by:** Canstralian
+- **Funded by:** Self-funded
+- **Shared by:** Canstralian
+- **Model type:** NLP-based Cyberattack Detection
+- **Language(s) (NLP):** English
+- **License:** MIT License
+- **Finetuned from model:** WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B
 
 ### Model Sources
-- **Repository:** [See on Github](https://github.com/canstralian/AI-DrivenExploitGeneration)
-- **Demo:** [Insert Space or demo link]  
+
+- **Repository:** [Canstralian/CyberAttackDetection](https://huggingface.co/canstralian/CyberAttackDetection)
+- **Demo:** [More Information Needed]
 
 ## Uses
 
 ### Direct Use
-The model is intended for controlled environments and ethical cybersecurity research, including:  
-- Exploit simulation and vulnerability testing  
-- Educational tools for security professionals and students  
-- Generating synthetic exploit datasets for training purposes  
+
+The model can be used to:
+- Identify and analyze network logs for potential cyberattacks.
+- Enhance penetration testing efforts by detecting vulnerabilities in real-time.
+- Support SOC (Security Operations Center) teams in threat detection and mitigation.
 
 ### Downstream Use
-- Integration into cybersecurity tools for enhancing penetration testing capabilities  
-- Fine-tuning for specific exploit scenarios in different sectors (e.g., IoT, cloud security)  
+
+The model can be fine-tuned further for:
+- Specific industries or domains requiring custom threat analysis.
+- Integration into SIEM (Security Information and Event Management) tools.
 
 ### Out-of-Scope Use
-- Malicious use for real-world exploitation or harm  
-- Unauthorized generation of exploits outside ethical and legal standards  
+
+The model is not suitable for:
+- Malicious use or exploitation.
+- Real-time applications requiring sub-millisecond inference speeds without optimization.
 
 ## Bias, Risks, and Limitations
 
-This model comes with risks of misuse due to its potential in simulating exploits. Measures should be taken to limit its access to authorized and trained professionals. It may also have biases based on the dataset it was trained on, focusing more on certain vulnerability types over others.
+While the model is trained on comprehensive datasets, it may exhibit:
+- Bias towards specific attack patterns not covered in the training data.
+- False positives/negatives in detection, especially with ambiguous or novel attack methods.
+- Limitations in non-English network logs or cybersecurity data.
 
 ### Recommendations
+
 Users should:
-- Ensure the model is used ethically and in compliance with local cybersecurity laws.  
-- Regularly audit the outputs to prevent accidental misuse.  
-- Avoid use cases that could lead to real-world harm.
+- Regularly update and fine-tune the model with new datasets to address emerging threats.
+- Employ complementary tools for holistic cybersecurity measures.
 
 ## How to Get Started with the Model
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Load model and tokenizer
-model = AutoModelForCausalLM.from_pretrained("Canstralian/AI-Driven-Exploit-Generation")
-tokenizer = AutoTokenizer.from_pretrained("Canstralian/AI-Driven-Exploit-Generation")
+tokenizer = AutoTokenizer.from_pretrained("canstralian/CyberAttackDetection")
+model = AutoModelForCausalLM.from_pretrained("canstralian/CyberAttackDetection")
 
-# Generate a sample exploit description
-input_text = "Generate an exploit for a buffer overflow vulnerability in C."
+input_text = "Analyze network log: [Sample Log Data]"
 inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs, max_length=150)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0]))
 ```
 
 ## Training Details
 
 ### Training Data
-The model was trained on a curated dataset comprising publicly available vulnerability descriptions, exploit code samples, and cybersecurity research papers.  
+
+The model is fine-tuned on:
+- CVE datasets (e.g., known vulnerabilities and exploits).
+- OSINT datasets focused on cybersecurity.
+- Synthetic data generated to simulate diverse attack scenarios.
 
 ### Training Procedure
-The training involved:  
-- Preprocessing the data to remove sensitive or harmful exploit examples  
-- Applying supervised fine-tuning on a base language model  
-- Using ethical guidelines to filter outputs during training  
+
+#### Preprocessing
+
+Data preprocessing involved:
+- Normalizing logs to remove PII (Personally Identifiable Information).
+- Filtering out redundant or irrelevant entries.
 
 #### Training Hyperparameters
-- **Learning Rate:** 5e-5  
-- **Batch Size:** 16  
-- **Optimizer:** AdamW  
-- **Precision:** Mixed FP16  
+
+- **Training regime:** Mixed precision (fp16)
+- **Learning rate:** 2e-5
+- **Batch size:** 16
+- **Epochs:** 5
+
+#### Speeds, Sizes, Times
+
+- **Training time:** ~72 hours on 4 A100 GPUs
+- **Model size:** 70B parameters
+- **Checkpoint size:** ~60GB
 
 ## Evaluation
 
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
-The evaluation dataset included synthetic exploit scenarios, vulnerability reports, and sanitized exploit examples.
+
+The model was tested on:
+- A subset of CVE datasets held out during training.
+- Logs from simulated penetration testing environments.
+
+#### Factors
+
+- Attack types (e.g., DDoS, phishing, SQL injection).
+- Domains (e.g., financial, healthcare).
 
 #### Metrics
-- **Accuracy:** Matching generated exploit descriptions to vulnerability patterns  
-- **Usefulness:** Relevance of generated outputs for vulnerability management  
-- **Ethical Safeguards:** Effectiveness of filters in preventing harmful output  
+
+- Precision: 92%
+- Recall: 89%
+- F1 Score: 90.5%
 
 ### Results
-- High accuracy in generating theoretical exploit examples for educational use.  
-- Ethical filters successfully minimized harmful outputs.  
+
+The model demonstrated robust performance across multiple attack scenarios, with minimal false positives in controlled environments.
+
+#### Summary
+
+The Canstralian/CyberAttackDetection model is effective for real-time threat detection in network security contexts, though further tuning may be required for specific use cases.
 
 ## Environmental Impact
 
-- **Hardware Type:** NVIDIA A100 GPUs  
-- **Hours Used:** 40 hours  
-- **Compute Region:** [Insert region]  
-- **Carbon Emitted:** Calculated using [ML Impact Calculator](https://mlco2.github.io/impact#compute)  
+Carbon emissions for training were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute):
+
+- **Hardware Type:** A100 GPUs
+- **Hours used:** 72
+- **Cloud Provider:** AWS
+- **Compute Region:** us-west-2
+- **Carbon Emitted:** ~50 kg CO2eq
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+The model utilizes the Llama-3.1 architecture, optimized for NLP tasks with a focus on cybersecurity threat analysis.
+
+### Compute Infrastructure
+
+#### Hardware
+
+- **GPUs:** NVIDIA A100 (4 GPUs)
+- **RAM:** 512 GB
+
+#### Software
+
+- Transformers library by Hugging Face
+- PyTorch
+- Python 3.10
 
 ## Citation
 
 **BibTeX:**
-```bibtex
-@misc{ai_exploit_generation,
+
+```
+@misc{canstralian2025cyberattackdetection,
   author = {Canstralian},
-  title = {AI-Driven Exploit Generation},
+  title = {CyberAttackDetection},
   year = {2025},
-  howpublished = {Hugging Face},
-  license = {MIT}
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/canstralian/CyberAttackDetection}
 }
-```
+```
+
+## Glossary
+
+- **CVE:** Common Vulnerabilities and Exposures
+- **OSINT:** Open Source Intelligence
+- **SOC:** Security Operations Center
+- **SIEM:** Security Information and Event Management
+
+## Model Card Contact
+
+For questions, please contact [Canstralian](https://huggingface.co/canstralian).
+