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A Framework for Budget-Constrained Zero-Day Cyber Threat Mitigation: A Knowledge-Guided Reinforcement Learning

Mainak Basak1, Geon-Yun Shin1

  • 1School of Computer Engineering & Applied Mathematics, Hankyong National University, Anseong-si 17579, Republic of Korea.

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Summary

This study introduces a novel knowledge-based cyber-defense framework that improves detection of unknown cyber-attacks. The system enhances accuracy and explainability within budget constraints, outperforming traditional methods.

Keywords:
Cyber-Threat Knowledge GraphMITRE ATT&CKgenerative cyber rangereinforcement learning for cyber defensezero-day TTP

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Knowledge Representation

Background:

  • Conventional machine learning defenses struggle with novel attack chains and lack explainability.
  • Limited telemetry budgets hinder the efficiency and auditing capabilities of current systems.

Purpose of the Study:

  • To develop a knowledge-based cyber-defense framework for improved detection of zero-day attacks.
  • To integrate ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) constrained generation, budget-constrained reinforcement learning, and causal explanation.

Main Methods:

  • Formalizing attack chain synthesis using a grammar-formalized ATT&CK database.
  • Compiling attacks into Zeek-aligned witness telemetry for efficient training.
  • Utilizing a Cyber-Threat Knowledge Graph (CTKG) for causal relations and decision enhancement.
  • Implementing a sensor budget policy for cost and latency-bound decisions.

Main Results:

  • Demonstrated significant improvements over conventional techniques in low-False Positive Rate (FPR) accuracy.
  • Achieved enhanced Time to Detect (TTD) and improved calibration for zero-day attack instances.
  • Provided traceable explanations for generated alarms through defense-provenance features.

Conclusions:

  • The proposed framework offers a robust and auditable pipeline for advanced cyber defense.
  • It effectively addresses limitations of conventional methods in detecting novel threats and managing resources.
  • The integration of knowledge graphs and causal reasoning enhances threat detection and response.