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Canstralian/CyberAttackDetection - AI Model Overview

Model Description

CyberAttackDetection is a cutting-edge machine learning model designed to detect and classify a wide range of cyberattacks in real-time. Built using advanced algorithms and a comprehensive dataset of known attack signatures, the model can effectively identify abnormal behaviors, intrusion attempts, and potential threats in network traffic and system logs.

The model is optimized for high accuracy and low latency, making it ideal for use in real-time network monitoring, incident response, and security operations centers. By leveraging WhiteRabbitNeo (based on Llama-3.1), it offers high adaptability to new attack vectors and ensures robust protection against both common and sophisticated threats.

Key Features:

  • Real-time detection and classification of cyberattacks
  • Identification of vulnerabilities and exploits, including zero-day attacks
  • Adaptive learning capabilities to recognize new threats
  • High accuracy and low false-positive rates
  • Scalable for deployment in diverse environments, from small businesses to large enterprises

This model is tailored for penetration testers, cybersecurity professionals, and organizations looking to enhance their security posture with AI-powered attack detection.

WhiteRabbitNeo License + Usage Restrictions

The CyberAttackDetection model is built using WhiteRabbitNeo, and it adheres to the Llama-3.1 License, with an extended version specific to WhiteRabbitNeo. By using this model, you agree to the following usage restrictions:

You may not use the model or its derivatives in any way that:

  • Violates any applicable national or international law or infringes upon third-party rights.
  • Is intended for military use or harm to minors.
  • Generates false information or disseminates inappropriate content.
  • Exploits or harms individuals based on protected characteristics.
  • Discriminates against individuals or groups based on personal characteristics or legal protections.

For further details on the licensing and restrictions, refer to the WhiteRabbitNeo License Agreement.

Topics Covered in Cyberattack Detection

The CyberAttackDetection model helps identify vulnerabilities that attackers commonly exploit, including but not limited to:

  • Open Ports: Identifying entry points like HTTP (80, 443), FTP (21), SSH (22), and SMB (445).
  • Outdated Software: Vulnerabilities arising from outdated systems and third-party services.
  • Default Credentials: Risks posed by common factory-installed usernames and passwords.
  • Misconfigurations: Insecure service configurations that can open up attack vectors.
  • Injection Flaws: Common web vulnerabilities like SQL injection, XSS, and command injections.
  • Unencrypted Services: Identifying services without encryption (e.g., HTTP vs HTTPS).
  • Known Software Vulnerabilities: Checking for outdated software vulnerabilities using resources like the NVD or tools like Nessus and OpenVAS.
  • Cross-Site Request Forgery (CSRF): Unauthorized command transmission in web apps.
  • API Vulnerabilities: Detecting insecure API endpoints and data leakage.
  • Denial of Service (DoS): Identifying DoS vulnerabilities that impact system availability.
  • Sensitive Data Exposure: Identifying vulnerabilities that expose personal or financial data.

Terms of Use

By accessing and using this AI model, you acknowledge that you are solely responsible for its usage and the outcomes that result. You agree to indemnify, defend, and hold harmless the creators and any affiliated entities from any liabilities, damages, or losses incurred as a result of using the model.

This AI model is provided "as is" and "as available" without any warranties, express or implied. The creators make no guarantee that the model will meet your requirements or be available without interruption, security breaches, or errors.

Disclaimer: Use this model at your own risk. The creators will not be liable for any damages, including loss of data or system failures, resulting from the use of this model.

Let me know if you need any more modifications!

Model Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Direct Use

This model can be used directly for detecting cyber attacks by analyzing network traffic or system logs. It is especially useful for network administrators and cybersecurity experts who need real-time or historical analysis of potentially malicious activities.

Downstream Use [optional]

The model can be fine-tuned further for specific types of cyber attacks or to suit different environments (e.g., enterprise networks, small businesses). It can also be integrated into larger security ecosystems that perform continuous monitoring and threat analysis.

Out-of-Scope Use

The model is not intended for detecting non-cyber attacks or for use outside cybersecurity applications. It may not perform well with highly specialized or obscure types of attacks that are not well-represented in the training data.

Bias, Risks, and Limitations

The model’s performance is influenced by the quality and diversity of the training data. Misclassifications may occur, particularly when encountering novel attack patterns or environments not well-represented in the dataset. Furthermore, the model may generate false positives or miss complex attack vectors.

Recommendations

Users should regularly update the model with new data and threat intelligence to keep it relevant. The model should be used in conjunction with human oversight and other detection mechanisms to minimize the risk of undetected threats.

How to Get Started with the Model

To get started with the model, use the following code:

from transformers import pipeline

model = pipeline("cyber_attack_detection", model="Canstralian/CyberAttackDetection")
# Example usage: Pass network traffic or system log data to the model
result = model("Example log data or network traffic")
print(result)

Training Details

Training Data

The model was trained using a combination of datasets related to penetration testing, shell commands, and wordlists, which are essential for recognizing attack vectors and behaviors in real-world environments.

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

  • Training regime: fp16 mixed precision

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

  • Pentesting Dataset: Used for testing the model’s ability to detect attack behaviors.
  • Shell Commands Dataset: Assessed the model’s effectiveness in recognizing shell-related attack commands.
  • Wordlists Dataset: Evaluated the model’s proficiency in detecting dictionary-based attacks.

Factors

The evaluation tests for the model’s ability to detect common attack vectors, unusual patterns, and malicious behaviors across different datasets.

Metrics

  • Accuracy
  • Precision
  • Recall
  • F1-Score

Results

[More Information Needed]

Summary

The model performs well at detecting common types of cyber attacks but is subject to limitations in environments where the attack types differ significantly from those seen in the training datasets.

Model Examination [optional]

[More Information Needed]

Environmental Impact

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
  • Cloud Provider: [More Information Needed]
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

The model uses deep learning techniques to classify and identify malicious patterns in system logs and network traffic.

Compute Infrastructure

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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