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--- |
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license: mit |
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language: |
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- en |
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auto_detected: true |
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datasets: |
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- Canstralian/pentesting_dataset |
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- Canstralian/Wordlists |
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- Canstralian/ShellCommands |
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- Canstralian/CyberExploitDB |
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- Chemically-motivated/CyberSecurityDataset |
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- Chemically-motivated/AI-Agent-Generating-Tool-Debugging-Prompt-Library |
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metrics: |
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- accuracy |
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- precision |
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- f1 |
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- code_eval |
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base_model: |
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- WhiteRabbitNeo/WhiteRabbitNeo-33B-v1.5 |
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library_name: transformers |
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tags: |
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- code |
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--- |
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# CyberAttackDetection |
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## Overview |
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The **CyberAttackDetection** model is a fine-tuned BERT-based sequence classification model designed to identify cyberattacks in textual descriptions. It classifies input data into two categories: |
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- **Attack (1)**: The text describes a cybersecurity threat or attack. |
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- **Non-Attack (0)**: The text does not describe a cybersecurity threat. |
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--- |
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## Model Details |
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- **License**: [MIT License](LICENSE) |
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- **Datasets**: |
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- Custom cybersecurity datasets: |
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- `Canstralian/pentesting_dataset` |
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- `Canstralian/Wordlists` |
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- `Canstralian/ShellCommands` |
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- `Canstralian/CyberExploitDB` |
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- `Chemically-motivated/CyberSecurityDataset` |
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- `Chemically-motivated/AI-Agent-Generating-Tool-Debugging-Prompt-Library` |
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- **Language**: English |
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- **Metrics**: |
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- **Accuracy**: 85% |
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- **F1 Score**: 0.83 |
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- **Precision**: 0.80 |
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- **Recall**: 0.87 |
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- **Base Model**: `WhiteRabbitNeo/WhiteRabbitNeo-33B-v1.5` |
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- **Pipeline Tag**: `text-classification` |
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- **Library Name**: `transformers` |
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- **Tags**: `cybersecurity`, `text-classification`, `attack-detection`, `BERT` |
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- **New Version**: `v1.0.0` |
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- **Auto-Detected Features**: True |
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--- |
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## Model Usage |
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### Installation |
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Before using the model, ensure the necessary dependencies are installed: |
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```bash |
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pip install transformers torch |
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``` |
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### Example Code |
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Use the following Python code to load the model and classify a sample text: |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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# Load the fine-tuned model and tokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("Canstralian/CyberAttackDetection") |
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tokenizer = AutoTokenizer.from_pretrained("Canstralian/CyberAttackDetection") |
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# Example input: Cyberattack description |
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text = "A vulnerability was discovered in the server software." |
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# Tokenize the input |
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inputs = tokenizer(text, return_tensors="pt") |
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# Get model predictions |
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outputs = model(**inputs) |
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# Predict the label (1 = attack, 0 = non-attack) |
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prediction = outputs.logits.argmax(dim=-1) |
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print(f"Prediction: {'Attack' if prediction.item() == 1 else 'Non-Attack'}") |
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``` |
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## Prompts: |
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- Open Ports: "Analyze the following network scan report and identify open ports and their associated vulnerabilities. Suggest best practices to secure these ports: [Insert network scan report]." |
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- Outdated Software or Services: "Given this list of installed software and services, identify outdated versions and known vulnerabilities. Provide recommendations for updates or patches to mitigate risks: [Insert software and service list]." |
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- Default Credentials: "Scan the following system configurations for any use of default credentials. Provide a list of affected services and recommendations for securing these credentials: [Insert system configuration details]." |
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- Misconfigurations: "Evaluate the provided system configuration for potential misconfigurations. Highlight risks and provide recommendations for secure setup: [Insert system configuration details]." |
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- Injection Flaws: "Review the given web application code or request logs and identify potential injection vulnerabilities such as SQL injection, command injection, or XSS. Provide remediation steps: [Insert code or logs]." |
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- Unencrypted Services: "Analyze the following network configuration and identify services that are transmitting data without encryption. Suggest strategies to enforce secure transmission: [Insert network configuration details]." |
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- Known Software Vulnerabilities: "Review the provided software inventory and cross-reference it with known vulnerabilities in the National Vulnerability Database (NVD). Recommend patches or workarounds: [Insert software inventory]." |
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- Cross-Site Request Forgery (CSRF): "Examine the provided web application code for potential CSRF vulnerabilities. Suggest specific coding or configuration techniques to prevent these attacks: [Insert code]." |
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- Insecure Direct Object References (IDOR): "Analyze the provided API endpoints and their associated access controls. Identify any IDOR vulnerabilities and suggest secure implementation strategies: [Insert API endpoint details]." |
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- Security Misconfigurations in Web Servers/Applications: "Assess the given web server configuration for security misconfigurations, such as improper HTTP headers or verbose error messages. Recommend changes to harden the server: [Insert server configuration]." |
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- Broken Authentication and Session Management: "Review the provided authentication and session management implementation. Identify weaknesses and recommend strategies to prevent compromise: [Insert authentication/session management details]." |
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- Sensitive Data Exposure: "Analyze the system's data handling processes and storage practices to identify potential sensitive data exposure. Recommend measures to protect sensitive information: [Insert system details]." |
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- API Vulnerabilities: "Examine the following API documentation and implementation for vulnerabilities, including insecure endpoints and data leakage. Provide recommendations for securing the API: [Insert API documentation]." |
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- Denial of Service (DoS) Vulnerabilities: "Review the system's architecture and configuration for potential vulnerabilities to DoS attacks. Suggest mitigation strategies such as rate limiting and load balancing: [Insert system architecture]." |
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- Buffer Overflows: "Analyze the provided code or application for buffer overflow vulnerabilities. Highlight potential weak points and recommend secure coding practices to prevent exploitation: [Insert code]." |
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## Model Training Details |
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### Training Objective |
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The model was fine-tuned to classify descriptive text as either an attack or non-attack event. It uses a **binary classification** approach. |
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### Training Data |
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- The training data includes cybersecurity-related attack descriptions and non-attack examples from curated datasets. |
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--- |
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## Evaluation |
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The model was evaluated on a balanced test set using the following metrics: |
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- **Accuracy**: 85% |
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- **F1 Score**: 0.83 |
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- **Precision**: 0.80 |
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- **Recall**: 0.87 |
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These results indicate strong performance in detecting cyberattacks from text. |
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--- |
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## License |
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This project is licensed under the **MIT License**. Refer to the [LICENSE](LICENSE) file for details. |
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--- |
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## How to Contribute |
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We welcome contributions! |
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- **Submit Issues**: If you encounter problems, open an issue on the repository. |
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- **Pull Requests**: Feel free to contribute code improvements or documentation updates. |
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--- |
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## Contact |
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For further information or inquiries, contact: **canstralian@cybersecurity.com** |
