Reza Fayyazi

Reza Fayyazi, Shanchieh Jay Yang

The volume, variety, and velocity of change in vulnerabilities and exploits have made incident threat analysis challenging with human expertise and experience along. Tactics, Techniques, and Procedures (TTPs) are to describe how and why attackers exploit vulnerabilities. However, a TTP description written by one security professional can be interpreted very differently by another, leading to confusion in cybersecurity operations or even business, policy, and legal decisions. Meanwhile, advancements in AI have led to the increasing use of Natural Language Processing (NLP) algorithms to assist the various tasks in cyber operations. With the rise of Large Language Models (LLMs), NLP tasks have significantly improved because of the LLM's semantic understanding and scalability. This leads us to question how well LLMs can interpret TTPs or general cyberattack descriptions to inform analysts of the intended purposes of cyberattacks. We propose to analyze and compare the direct use of LLMs (e.g., GPT-3.5) versus supervised fine-tuning (SFT) of small-scale-LLMs (e.g., BERT) to study their capabilities in predicting ATT&CK tactics. Our results reveal that the small-scale-LLMs with SFT provide a more focused and clearer differentiation between the ATT&CK tactics (if such differentiation exists). On the other hand, direct use of LLMs offer a broader interpretation of cyberattack techniques. When treating more general cases, despite the power of LLMs, inherent ambiguity exists and limits their predictive power. We then summarize the challenges and recommend research directions on LLMs to treat the inherent ambiguity of TTP descriptions used in various cyber operations.

Reza Fayyazi, Rozhina Taghdimi, Shanchieh Jay Yang

Tactics, Techniques, and Procedures (TTPs) outline the methods attackers use to exploit vulnerabilities. The interpretation of TTPs in the MITRE ATT&CK framework can be challenging for cybersecurity practitioners due to presumed expertise and complex dependencies. Meanwhile, advancements with Large Language Models (LLMs) have led to recent surge in studies exploring its uses in cybersecurity operations. It is, however, unclear how LLMs can be used in an efficient and proper way to provide accurate responses for critical domains such as cybersecurity. This leads us to investigate how to better use two types of LLMs: small-scale encoder-only (e.g., RoBERTa) and larger decoder-only (e.g., GPT-3.5) LLMs to comprehend and summarize TTPs with the intended purposes (i.e., tactics) of a cyberattack procedure. This work studies and compares the uses of supervised fine-tuning (SFT) of encoder-only LLMs vs. Retrieval Augmented Generation (RAG) for decoder-only LLMs (without fine-tuning). Both SFT and RAG techniques presumably enhance the LLMs with relevant contexts for each cyberattack procedure. Our studies show decoder-only LLMs with RAG achieves better performance than encoder-only models with SFT, particularly when directly relevant context is extracted by RAG. The decoder-only results could suffer low `Precision' while achieving high `Recall'. Our findings further highlight a counter-intuitive observation that more generic prompts tend to yield better predictions of cyberattack tactics than those that are more specifically tailored.