An in-vehicle digital twin framework using temporal convolutional networks and hierarchical navigable small world algorithms detects Sybil attacks with 0.984 accuracy and reduces near-collision metrics by 72-88% on real-world field data.
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HySecTwin adds semantic modeling and hybrid rule-plus-fuzzy reasoning to digital twins so they can detect and explain cyber threats in cyber-physical systems faster than rule-only methods.
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In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection
An in-vehicle digital twin framework using temporal convolutional networks and hierarchical navigable small world algorithms detects Sybil attacks with 0.984 accuracy and reduces near-collision metrics by 72-88% on real-world field data.
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HySecTwin: A Knowledge-Driven Digital Twin Framework Augmented with Hybrid Reasoning for Cyber-Physical Systems
HySecTwin adds semantic modeling and hybrid rule-plus-fuzzy reasoning to digital twins so they can detect and explain cyber threats in cyber-physical systems faster than rule-only methods.