Cybersecurity
认知网络防御数字孪生
Cognitive Cyber Defense Digital TwinA unified architecture integrating federated learning, graph neural networks, and autonomous SOAR to create a predictive, privacy-preserving digital replica of organizational assets for autonomous threat prediction and response. / 一种集成联邦学习、图神经网络和自主SOAR的统一架构，用于创建组织资产的预测性、隐私保护数字副本，以实现自主威胁预测和响应。
- Knowledge type
- 实际应用
- First proposed or discovered
- 2028
- Proposer
- Sivaramakrishnan Narayanan
- Creator
- 农场主
- Last updated
- 2026-10-08 07:57:45
Sub-knowledge points
Federated Learning (FL) for cross-organizational privacy-preserving intelligenceGraph Neural Network (GNN) for multi-stage lateral movement predictionAdversarially hardened detection modelsAutonomous Security Orchestration, Automation, and Response (SOAR)Deception engineering integrationExplainable AI (XAI) for compliance reporting
Higher-order knowledge points
Reactive SOC architecturesSignature-dependent securityTraditional cyber defense baselines
Main parameters and performance indicators
Federated Learning Mesh: Privacy-preserving gradient sharing mechanismGraph Neural Network: Attack-path forecasting engineReinforcement Learning: Red agent stress-testing mechanismExplainable AI (XAI): SHAP-based audit reporting module
Open problems
Scalability of federated intelligence mesh across heterogeneous enterprise networksAdversarial robustness against evolving zero-day exploitsReal-time synchronization latency in hybrid cloud environments
Related literature
ranked by community starsCyber Defense Digital Twins: A Federated Learning and Zero-Trust AI Architecture for Autonomous Threat Prediction and ResponseOpen source
Related products and links
Cognitive Cyber Defense Digital Twin (CCDT)Federated Intelligence MeshAdversarially hardened detection models
Creation and modification trail
1 recordsRecord title: Cognitive Cyber Defense Digital Twin.
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