Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning

TL;DR

This paper introduces C-MADF, a structurally constrained, causal multi-agent reinforcement learning framework for autonomous cyber defense, significantly reducing false positives and improving detection accuracy in real-world datasets.

Contribution

It combines causal modeling with adversarial dual-policy reinforcement learning to create a transparent, constrained decision-making process for autonomous cyber defense.

Findings

C-MADF reduces false-positive rates from around 10% to 1.8%.

Achieves 0.997 precision, 0.961 recall, and 0.979 F1-score on CICIoT2023 dataset.

Provides a human-in-the-loop interface with explainability and transparency scores.

Abstract

Autonomous agents are increasingly deployed in both offensive and defensive cyber operations, creating high-speed, closed-loop interactions in critical infrastructure environments. Advanced Persistent Threat (APT) actors exploit "Living off the Land" techniques and targeted telemetry perturbations to induce ambiguity in monitoring systems, causing automated defenses to overreact or misclassify benign behavior as malicious activity. Existing monolithic and multi-agent defense pipelines largely operate on correlation-based signals, lack structural constraints on response actions, and are vulnerable to reasoning drift under ambiguous or adversarial inputs. We present the Causal Multi-Agent Decision Framework (C-MADF), a structurally constrained architecture for autonomous cyber defense that integrates causal modeling with adversarial dual-policy control. C-MADF first learns a Structural Causal Model (SCM) from historical telemetry and compiles it into an investigation-level Directed Acyclic Graph (DAG) that defines admissible response transitions. This roadmap is formalized as a Markov Decision Process (MDP) whose action space is explicitly restricted to causally consistent transitions. Decision-making within this constrained space is performed by a dual-agent reinforcement learning system in which a threat-optimizing Blue-Team policy is counterbalanced by a conservatively shaped Red-Team policy. Inter-policy disagreement is quantified through a Policy Divergence Score and exposed via a human-in-the-loop interface equipped with an Explainability-Transparency Score that serves as an escalation signal under uncertainty. On the real-world CICIoT2023 dataset, C-MADF reduces the false-positive rate from 11.2%, 9.7%, and 8.4% in three cutting-edge literature baselines to 1.8%, while achieving 0.997 precision, 0.961 recall, and 0.979 F1-score.