Revolutionizing Cyber Threat Detection with Large Language Models
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Natural Language Processing (NLP) domain is experiencing a revolution due to the capabilities of Pre-trained Large Language Models ( LLMs), fueled by ground-breaking Transformers architecture, resulting into unprecedented advancements. Their exceptional aptitude for assessing probability distributions of text sequences is the primary catalyst for outstanding improvement of both the precision and efficiency of NLP models. This paper introduces for the first time SecurityLLM, a pre-trained language model designed for cybersecurity threats detection. The SecurityLLM model is articulated around two key generative elements: SecurityBERT and FalconLLM. SecurityBERT operates as a cyber threat detection mechanism, while FalconLLM is an incident response and recovery system. To the best of our knowledge, SecurityBERT represents the inaugural application of BERT in cyber threat detection. Despite the unique nature of the input data and features, such as the reduced significance of syntactic structures in content classification, the suitability of BERT for this duty demonstrates unexpected potential, thanks to our pioneering study. We reveal that a simple classification model, created from scratch, and consolidated with LLMs, exceeds the performance of established traditional Machine Learning (ML) and Deep Learning (DL) methods in cyber threat detection, like Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN). The experimental analysis, conducted using a collected cybersecurity dataset, proves that our SecurityLLM model can identify fourteen (14) different types of attacks with an overall accuracy of 98
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