Applications of Large Language Models in Cybersecurity

Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP), demonstrating

advanced capabilities in text interpretation and generation. In this thesis, we systematically explore their potential 

to enhance cybersecurity solutions, focusing on three key applications: text classification, data augmentation, and 

synthetic data generation. Within the context of spam detection and policy compliance checking, two representative 

NLP tasks in cybersecurity, we assess the effectiveness of state-of-the-art LLMs against various supervised Machine 

Learning (ML) techniques. Our findings reveal the proficiency of LLMs in text classification without the need for 

task-specific fine-tuning, making them particularly suitable in scenarios with limited data. However, we also identify 

some limitations of current LLMs, including prompt sensitivity and classification biases. Furthermore, LLMs demonstrate 

efficiency in augmenting existing text data and generating diverse synthetic datasets. Our experiments indicate that 

Deep Learning (DL) models benefit most from such data, which is a promising aspect, considering their high data demands. 

Nevertheless, these approaches also pose challenges such as bias propagation, stylistic shifts, and potential for malicious 

use. Future work should address these issues and explore a broader range of cybersecurity problems to fully harness the 

potential of LLMs. We conclude that despite the challenges they present, LLMs hold significant promise in advancing the field 

of cybersecurity.