MOBAL learns a model of an ongoing cyberattack with Bayesian updates and computes incident responses with a quantized version of that model, giving robustness to model misspecification on CAGE-2.
Toward an Intent-Based and Ontology-Driven Autonomic Security Response in Security Orchestration Automation and Response
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abstract
Modern Security Orchestration, Automation, and Response (SOAR) platforms must rapidly adapt to continuously evolving cyber attacks. Intent-Based Networking has emerged as a promising paradigm for cyber attack mitigation through high-level declarative intents, which offer greater flexibility and persistency than procedural actions. In this paper, we bridge the gap between two active research directions: Intent-Based Cyber Defense and Autonomic Cyber Defense, by proposing a unified, ontology-driven security intent definition leveraging the MITRE-D3FEND cybersecurity ontology. We also propose a general two-tiered methodology for integrating such security intents into decision-theoretic Autonomic Cyber Defense systems, enabling hierarchical and context-aware automated response capabilities. The practicality of our approach is demonstrated through a concrete use case, showcasing its integration within next-generation Security Orchestration, Automation, and Response platforms.
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Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization
MOBAL learns a model of an ongoing cyberattack with Bayesian updates and computes incident responses with a quantized version of that model, giving robustness to model misspecification on CAGE-2.