Automated Attack Testflow Extraction from Cyber Threat Report using BERT for Contextual Analysis

TL;DR

This paper introduces FLOWGUARDIAN, a BERT-based NLP system that automates extraction of attack testflows from threat reports, improving speed and accuracy in cybersecurity threat analysis.

Contribution

It presents a novel NLP approach using BERT to automate attack testflow extraction, reducing manual effort and errors in cybersecurity report analysis.

Findings

High accuracy in extracting attack sequences

Significant reduction in analysis time

Enhanced coverage of attack scenarios

Abstract

In the ever-evolving landscape of cybersecurity, the rapid identification and mitigation of Advanced Persistent Threats (APTs) is crucial. Security practitioners rely on detailed threat reports to understand the tactics, techniques, and procedures (TTPs) employed by attackers. However, manually extracting attack testflows from these reports requires elusive knowledge and is time-consuming and prone to errors. This paper proposes FLOWGUARDIAN, a novel solution leveraging language models (i.e., BERT) and Natural Language Processing (NLP) techniques to automate the extraction of attack testflows from unstructured threat reports. FLOWGUARDIAN systematically analyzes and contextualizes security events, reconstructs attack sequences, and then generates comprehensive testflows. This automated approach not only saves time and reduces human error but also ensures comprehensive coverage and robustness in cybersecurity testing. Empirical validation using public threat reports demonstrates FLOWGUARDIAN's accuracy and efficiency, significantly enhancing the capabilities of security teams in proactive threat hunting and incident response.