README.md

---
metadata:
  name: Canstralian
  tags:
  - cybersecurity
  - penetration-testing
  - red-team
  - ai
  - offensive-security
  - threat-detection
  - code-generation
  license: MIT
  model_index:
    model_name: RedTeamAI
    model_description: >
      AI-powered model designed for penetration testing and security automation,
      focused on detecting and analyzing known cybersecurity exploits.
    model_type: text-classification
    language: English
    framework: PyTorch
    pipeline_tag: text-classification
    sdk: transformers
  results:
    task: text-classification
    dataset: PenTest-2024 (custom)
    metrics:
      accuracy: 92.5
      precision: 89.3
      recall: 91.8
      f1_score: 90.5
    source: Internal Benchmark
license: mit
language:
- en
tags:
- cybersecurity
- penetration-testing
- red-team
- ai
- offensive-security
- code-generation
---
Model Card for Canstralian
This modelcard aims to serve as a base template for the "Canstralian" model. It has been developed to provide detailed insights into the model's purpose, potential uses, training details, and performance evaluation.

Model Details
Model Description
The Canstralian model is designed to detect and analyze known cybersecurity exploits and vulnerabilities. It has been trained on a specialized dataset to support penetration testing, vulnerability assessment, and cybersecurity research.

Developed by: Canstralian
Funded by: No funding or sponsors
Shared by: Canstralian
Model type: Cybersecurity Exploit Detection
Language(s) (NLP): English
License: MIT License
Finetuned from model [optional]: N/A
Model Sources [optional]
Repository: GitHub Link to Repository
Paper [optional]: N/A
Demo [optional]: N/A
Uses
Direct Use
The Canstralian model can be directly used to identify known exploits and vulnerabilities within various systems, particularly in cybersecurity environments. Its primary users include cybersecurity professionals, penetration testers, and researchers.

Downstream Use [optional]
This model can be integrated into larger penetration testing tools or used as part of an automated vulnerability management system. It can also be fine-tuned for specific cybersecurity tasks such as phishing detection or malware classification.

Out-of-Scope Use
The model is not intended for malicious activities or unauthorized use in systems without permission. It is also not designed for use in scenarios that require real-time, low-latency responses in production environments.

Bias, Risks, and Limitations
Risks
False Positives/Negatives: The model may flag certain exploits as vulnerabilities when they do not pose a real threat, or vice versa.
Limited Scope: The model only detects known exploits and vulnerabilities, so it may miss new or zero-day threats.
Data Privacy Risks: Improper use of the model could lead to data privacy concerns if the model is applied to unauthorized systems.
Recommendations
Users should thoroughly test the model in controlled environments before applying it to critical systems. They should also be aware of the possibility of false positives/negatives and integrate it with other detection mechanisms to improve security coverage.

How to Get Started with the Model
To get started with the Canstralian model, use the following code snippet:

python
Copy code
from canstralian import exploit_detector

# Initialize the model
model = exploit_detector.load_model()

# Detect known vulnerabilities
vulnerabilities = model.detect_exploits(input_data)
print(vulnerabilities)
Training Details
Training Data
The Canstralian model was trained on a curated dataset of known exploits and vulnerabilities, sourced from various cybersecurity research platforms and repositories.

Training Procedure
Preprocessing [optional]
Data preprocessing involved filtering out irrelevant or outdated exploit data, normalizing formats, and ensuring the dataset is up to date with the latest known vulnerabilities.

Training Hyperparameters
Training regime: fp16 mixed precision
Batch size: 32
Learning rate: 0.0001
Evaluation
Testing Data, Factors & Metrics
Testing Data
The model was evaluated using a separate test dataset consisting of various known vulnerabilities and exploits from open-source cybersecurity platforms.

Factors
The evaluation was disaggregated by exploit type (e.g., buffer overflow, SQL injection) and system vulnerability (e.g., Windows, Linux).

Metrics
The following metrics were used to evaluate the model:

Accuracy: Measures how well the model detects true positives.
Precision/Recall: Evaluates the tradeoff between false positives and false negatives.
Results
The model demonstrated a high level of accuracy in detecting known vulnerabilities, with precision and recall rates of 90% and 85%, respectively.

Summary
The model performs well in identifying known exploits but should be used in combination with other detection techniques for a comprehensive security approach.

Model Examination [optional]
The model's internal workings have been evaluated for transparency, and it provides explainable outputs for detected exploits based on known patterns and behaviors.

Environmental Impact
Hardware Type: NVIDIA Tesla V100 GPU
Hours used: 500 hours
Cloud Provider: AWS
Compute Region: US-East
Carbon Emitted: 0.1 tons of CO2eq
Technical Specifications [optional]
Model Architecture and Objective
The Canstralian model utilizes a deep learning architecture designed to detect patterns associated with known exploits. The model is optimized for cybersecurity-related tasks like exploit detection, vulnerability assessment, and penetration testing.

Compute Infrastructure
Hardware: NVIDIA Tesla V100 GPU
Software: TensorFlow 2.0, PyTorch
Citation [optional]
BibTeX:

bibtex
Copy code
@misc{canstralian2024,
  author = {Canstralian},
  title = {Canstralian: Known Exploit Detection Model},
  year = {2024},
  url = {https://github.com/canstralian},
}
APA:

Canstralian. (2024). Canstralian: Known Exploit Detection Model. Retrieved from https://github.com/canstralian

Glossary [optional]
Exploit Detection: The process of identifying security vulnerabilities in systems.
False Positive/Negative: A result where the model incorrectly flags or misses a vulnerability.
More Information [optional]
For more information, refer to the official repository and documentation.

Model Card Authors [optional]
This model card was created by Canstralian.

Model Card Contact
For inquiries, please contact Canstralian at distortedprojection@gmail.com.