Data Collection
Obtain data from publicly available cybersecurity datasets (such as CICIDS2017, CSE-CIC-IDS2018) and combine it with internal enterprise threat logs to build a diverse training set.
Model Fine-Tuning
Utilize GPT-4's fine-tuning capabilities to optimize the model for threat detection tasks, focusing on its contextual understanding and ability to identify anomalous behavior.
Experimental Validation
Test the model's performance in a simulated network environment, evaluating its detection accuracy, false positive rate, and response time.
Result Analysis
Compare the performance of GPT-4 with GPT-3.5 to analyze the improvement brought by fine-tuning.
Expected outcomes
Validating the effectiveness of GPT-4 in threat detection tasks, particularly its advantages in identifying complex and novel threats.
Providing quantitative analysis on the performance improvement brought by model fine-tuning, offering theoretical support for future AI applications in cybersecurity.
Promoting the deployment of OpenAI models in real-world cybersecurity scenarios, helping organizations respond to cyber threats more efficiently.
