A Review of Advancements and Applications of Pre-trained Language Models in Cybersecurity

Abstract

In this paper, we delve into the transformative role of pre-trained language models (PLMs) in cybersecurity, offering a comprehensive examination of their deployment across a wide array of cybersecurity tasks. Beginning with an exploration of general PLMs, including advancements and the emergence of domain-specific models tailored for cybersecurity, we provide an insightful overview of the foundational technologies driving these developments. The core of our review focuses on the multifaceted applications of PLMs in cybersecurity, ranging from malware and vulnerability detection to more nuanced areas like log analysis, network traffic analysis, and threat intelligence, among others. We also highlight recent strides in the application of large language models (LLMs), showcasing their growing influence in enhancing cybersecurity measures. By charting the landscape of PLM applications and pointing toward future directions, this work serves as a valuable resource for both the research community and industry practitioners, underlining the critical need for continued innovation and exploration in harnessing PLMs to fortify cybersecurity defenses.

Recommended citation: Liu, Zefang. "A Review of Advancements and Applications of Pre-trained Language Models in Cybersecurity." 2024 12th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2024.
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