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Canstralian/
AI-DrivenExploitGeneration

transformers

code

huzaifas-sidhpurwala/RedHat-security-VeX

cw1521/ember2018-malware

rr4433/Powershell_Malware_Detection_Dataset

PurCL/malware-top-100

10.57967/hf/4261

mit

endpoints_compatible

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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 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: Canstralian/CyberAttackDetection
Demo: [More Information Needed]

Uses

Direct Use

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

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

The model is not suitable for:

Malicious use or exploitation.
Real-time applications requiring sub-millisecond inference speeds without optimization.

Bias, Risks, and Limitations

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:

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 AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("canstralian/CyberAttackDetection")
model = AutoModelForCausalLM.from_pretrained("canstralian/CyberAttackDetection")

input_text = "Analyze network log: [Sample Log Data]"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

Training Details

Training Data

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

Preprocessing

Data preprocessing involved:

Normalizing logs to remove PII (Personally Identifiable Information).
Filtering out redundant or irrelevant entries.

Training Hyperparameters

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 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

Precision: 92%
Recall: 89%
F1 Score: 90.5%

Results

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

Carbon emissions for training were estimated using the Machine Learning Impact calculator:

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:

@misc{canstralian2025cyberattackdetection,
  author = {Canstralian},
  title = {CyberAttackDetection},
  year = {2025},
  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.

Canstralian/AI-DrivenExploitGeneration