This article explores the potential of large language models (LLMs) to transform dataset creation and analysis in cybersecurity. The proposed method leverages LLMs to overcome the labeled data bottleneck by generating high-quality, task-specific datasets for AI model tuning. Existing network intrusion analysis datasets are synthesized with domain knowledge extracted from cybersecurity literature to create a new dataset tailored for supervised training of zero-day exploit detection systems. LLMs interpret the semantic content of relevant literature to identify crucial characteristics and values of zero-day exploit signatures in network traffic. The resulting synthesized dataset is primarily based on 'organic' data collected by genuine sensors, with key feature characteristics intelligently interpolated by LLMs. This approach enables the creation of suitable training data for high-performance ML models. This article demonstrates the effectiveness of this method by utilizing advanced AI techniques to generate a dataset for zero-day exploit detection, illustrating the potential for accelerated progress in specialized AI for cybersecurity. The proposed solution offers a promising approach to address the challenge of labeled data scarcity in developing specialized AI for cybersecurity, facilitating more efficient and effective protection against emerging threats.
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