Advancements in Cybersecurity and Large Language Models

The field of cybersecurity is witnessing a significant shift towards leveraging large language models (LLMs) to enhance threat understanding and defense mechanisms. Recent research has focused on developing novel frameworks that utilize LLMs to analyze system telemetry and infer attacker intent, as well as designing honeypots that incorporate LLMs to improve context awareness and engagement. Additionally, there is a growing interest in exploring the applications of LLMs in network intrusion detection, password guessing, and conversation recovery from encrypted network traffic. These advancements have the potential to revolutionize the field of cybersecurity, enabling more effective and adaptive defense strategies.

Noteworthy papers in this area include: Security Logs to ATT&CK Insights, which proposes a framework for leveraging LLMs to analyze system logs and infer attacker actions. SBASH, which introduces a framework for designing and evaluating LLM-based honeypots. NetEcho, which presents a novel framework for recovering conversations from encrypted network traffic. Advances in these areas are expected to continue, with a focus on addressing the challenges and limitations of current approaches and exploring new applications for LLMs in cybersecurity.

Sources

Security Logs to ATT&CK Insights: Leveraging LLMs for High-Level Threat Understanding and Cognitive Trait Inference

SBASH: a Framework for Designing and Evaluating RAG vs. Prompt-Tuned LLM Honeypots

Advancing Honeywords for Real-World Authentication Security

KAPG: Adaptive Password Guessing via Knowledge-Augmented Generation

Network Intrusion Detection: Evolution from Conventional Approaches to LLM Collaboration and Emerging Risks

NetEcho: From Real-World Streaming Side-Channels to Full LLM Conversation Recovery

Adversarial Pre-Padding: Generating Evasive Network Traffic Against Transformer-Based Classifiers

SoK: Honeypots & LLMs, More Than the Sum of Their Parts?

Built with on top of