REVIEW 2 cited by
Autonomous Intelligent Cyber-defense Agent (AICA) Reference Architecture. Release 2.0
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
This report - a major revision of its previous release - describes a reference architecture for intelligent software agents performing active, largely autonomous cyber-defense actions on military networks of computing and communicating devices. The report is produced by the North Atlantic Treaty Organization (NATO) Research Task Group (RTG) IST-152 "Intelligent Autonomous Agents for Cyber Defense and Resilience". In a conflict with a technically sophisticated adversary, NATO military tactical networks will operate in a heavily contested battlefield. Enemy software cyber agents - malware - will infiltrate friendly networks and attack friendly command, control, communications, computers, intelligence, surveillance, and reconnaissance and computerized weapon systems. To fight them, NATO needs artificial cyber hunters - intelligent, autonomous, mobile agents specialized in active cyber defense. With this in mind, in 2016, NATO initiated RTG IST-152. Its objective has been to help accelerate the development and transition to practice of such software agents by producing a reference architecture and technical roadmap. This report presents the concept and architecture of an Autonomous Intelligent Cyber-defense Agent (AICA). We describe the rationale of the AICA concept, explain the methodology and purpose that drive the definition of the AICA Reference Architecture, and review some of the main features and challenges of AICAs.
Forward citations
Cited by 2 Pith papers
-
Streamlining Resilient Kubernetes Autoscaling with Multi-Agent Systems via an Automated Online Design Framework
KARMA uses four cooperating reinforcement-learning agents, each guided by a role and mission, to autoscale a Kubernetes cluster, and beats three existing autoscalers in a single-node simulation across failure scenarios.
-
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications
A narrative survey of multi-agent reinforcement learning for cyber defense, reviewing game-theoretic models, cyber gyms, and applications, concluding MARL is promising but faces scalability and simulation-to-real tran...
Discussion (0). Sign in to comment.