cozyiii/llama-aingfw
The cozyiii/llama-aingfw is an 8 billion parameter Llama-based model fine-tuned for network intrusion detection. This model specializes in classifying network traffic into specific attack categories such as DoS, bruteforce, web_attack, botnet, reconnaissance, and other attacks, alongside normal traffic. It leverages structured network flow features to identify and categorize various cyber threats. Its primary application is in research, testing, and educational demonstrations of AI-assisted network security systems.
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Model Overview
The cozyiii/llama-aingfw is an 8 billion parameter Llama-based model specifically fine-tuned for network intrusion detection (NID) tasks. It utilizes structured network flow features to classify traffic, making it a specialized tool for cybersecurity applications.
Key Capabilities
- Network Traffic Classification: The model is designed to categorize network traffic into distinct classes, identifying both normal behavior and various types of malicious activities.
- Intrusion Detection: It can detect and classify several common intrusion types, including:
normaldos(Denial of Service)bruteforceweb_attackbotnetreconnaissanceother_attack
- Feature-based Analysis: The model processes structured network flow features (e.g., Port, Duration, Fwd Packets, Bytes/s, SYN, ACK) to make its classifications.
Intended Use Cases
This model is particularly well-suited for:
- Research: Exploring and developing new methodologies in AI-driven network security.
- Testing: Evaluating the effectiveness of AI models in identifying network intrusions.
- Educational Demonstrations: Showcasing the practical application of large language models in cybersecurity contexts, specifically for intrusion detection systems.