Cyberally: Leveraging LLMS and knowledge graphs to empower cyber defenders

Document Type

Conference Proceeding

Publication Title

WWW Companion 2025 Companion Proceedings of the ACM Web Conference 2025

First Page

2851

Last Page

2854

Publisher

Association for Computing Machinery

School

School of Science

RAS ID

82132

Funders

Cyber Security Research Centre Limited

Comments

Kim, M., Wang, J., Moore, K., Goel, D., Wang, D., Mohsin, A., Ibrahim, A., Doss, R., Camtepe, S., & Janicke, H. (2025). Cyberally: Leveraging LLMS and knowledge graphs to empower cyber defenders. Companion Proceedings of the ACM on Web Conference 2025, 2851-2854. https://doi.org/10.1145/3701716.3715171

Abstract

The increasing frequency and sophistication of cyberattacks demand innovative approaches to strengthen defense capabilities. Training on live infrastructure poses significant risks to organizations, making secure, isolated cyber ranges an essential tool for conducting Red vs. Blue Team training events. These events enable security teams to refine their skills without impacting operational environments. While such training provides a strong foundation, the ever-evolving nature of cyber threats necessitates additional support for effective defense. To address this challenge, we introduce CyberAlly, a knowledge graph-enhanced AI assistant designed to enhance the efficiency and effectiveness of Blue Teams during incident response. Integrated into our cyber range alongside an open-source SIEM platform, CyberAlly monitors alerts, tracks Blue Team actions, and suggests tailored mitigation recommendations based on insights from prior Red vs. Blue Team exercises. This demonstration highlights the feasibility and impact of CyberAlly in augmenting incident response and equipping defenders to tackle evolving threats with greater precision and confidence.

DOI

10.1145/3701716.3715171

Access Rights

free_to_read

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Link to publisher version (DOI)

10.1145/3701716.3715171