The field of cybersecurity is witnessing a significant shift towards the adoption of Large Language Models (LLMs) to enhance threat detection, vulnerability assessment, and incident response. Recent developments have focused on leveraging LLMs to improve the security and robustness of various systems, including financial graphs, cybersecurity education, and digital twins. Notably, researchers have explored the use of LLMs for in-context learning, synthetic CTI generation, and automated STIX entity and relationship extraction. Furthermore, studies have investigated the application of LLMs in predictive maintenance, digital evidence discovery, and cybersecurity education. Overall, the field is moving towards the development of more efficient, scalable, and secure LLM-based solutions for cybersecurity applications. Noteworthy papers include: PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training, which presents a novel PRM-free security alignment framework. SynthCTI: LLM-Driven Synthetic CTI Generation to enhance MITRE Technique Mapping, which introduces a data augmentation framework for generating high-quality synthetic CTI sentences.
Advances in Large Language Models for Cybersecurity
Sources
Fake or Real: The Impostor Hunt in Texts for Space Operations
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training
Exploring the In-Context Learning Capabilities of LLMs for Money Laundering Detection in Financial Graphs
From Text to Actionable Intelligence: Automating STIX Entity and Relationship Extraction
SynthCTI: LLM-Driven Synthetic CTI Generation to enhance MITRE Technique Mapping
Enabling Cyber Security Education through Digital Twins and Generative AI
RECALLED: An Unbounded Resource Consumption Attack on Large Vision-Language Models
Information Security Based on LLM Approaches: A Review
Digital Twin Technologies in Predictive Maintenance: Enabling Transferability via Sim-to-Real and Real-to-Sim Transfer
Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery