Towards Automatic Hands-on-Keyboard Attack Detection Using LLMs in EDR Solutions

TL;DR

This paper proposes a novel LLM-based approach for detecting Hands-on-Keyboard cyberattacks in EDR systems by converting endpoint data into narratives for analysis, showing potential to outperform traditional methods.

Contribution

It introduces a new method that leverages LLMs to analyze narrative representations of endpoint data for improved attack detection in cybersecurity.

Findings

LLM-based models outperform traditional machine learning methods.

Narrative conversion enhances interpretability of endpoint data.

Dual training strategy improves detection accuracy.

Abstract

Endpoint Detection and Remediation (EDR) platforms are essential for identifying and responding to cyber threats. This study presents a novel approach using Large Language Models (LLMs) to detect Hands-on-Keyboard (HOK) cyberattacks. Our method involves converting endpoint activity data into narrative forms that LLMs can analyze to distinguish between normal operations and potential HOK attacks. We address the challenges of interpreting endpoint data by segmenting narratives into windows and employing a dual training strategy. The results demonstrate that LLM-based models have the potential to outperform traditional machine learning methods, offering a promising direction for enhancing EDR capabilities and apply LLMs in cybersecurity.