AI Driven Cyber Security Framework for Next Generation Optical Communication Networks

Abstract

Next generation optical communication networks will be progressively more programmable, software based, elastic, multi-domain and highly-connected with AI-intensive cloud and edge workloads. This progress expands capabilities and automation, but also expands cyber-physical attack surface in terms of optical physical layer, management plane, control plane and AI analytics pipeline. This paper describes an Artificial Intelligence-Driven Cyber Security Framework (AI-DCF) for future optical communication networks. This framework integrates optical telemetry, machine-learning-based anomaly identification, zero-trust access restriction, secure network governance, cryptographic agility, post-quantum capability and quantum-key-distribution aware key services into a unified framework. This paper applies a theoretical framework based on existing literature to consolidate recent research on smart optical networking, optical-layer attack detection, SDN security, adversarial machine learning, and quantum-safe security standards. The solution presents a framework that includes five nested layers (governance and risk; AI security analytics; optical-control security; optical-physical security; crypto-agile resilience). Results are represented in a threat taxonomy, a framework architecture, evidence mapping, and capability assessment tables. The study found that AI-based optical security needs to be approached not as an intrusion detection solution per se, but as a closed-loop, validating and standards-aligned cyber-physical system. The work provides insight for researchers, network organizations, cloud providers, and telecom operators in creating hardened optical infrastructures for 6G, AI data centers, smart cities and other critical digital services. In response to implementation-focused review feedback, the revised version also adds a small synthetic simulation, a proof-of-concept deployment architecture, an ML workflow demo, and an illustrative attack-detection example to demonstrate how the framework may be operationalized before field-test validation.