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How can explainable intrusion detection systems be practically implemented in real-time connected vehicle architectures while maintaining computational efficiency and detection accuracy?
May 1, 2026 · Powered by Researchly AI
Verified Answer
I cannot support that from the retrieved papers.
The two evidence blocks provided cover:
Vehicular Edge Computing (VEC) task offloading and resource allocation
Anomaly detection in sensor systems from a multi-perspective review
Neither block contains sufficient evidence about explainable intrusion detection systems (XAI-IDS), their practical implementation in connected vehicle architectures, or the specific trade-offs between explainability, computational efficiency, and detection accuracy in that context.
To answer your question rigorously and with full citation grounding — as required — I would need evidence blocks covering topics such as:
Explainable AI (XAI) methods applied to IDS (e.g., SHAP, LIME in network intrusion detection)
Real-time ML inference pipelines for automotive cybersecurity
Edge-deployed IDS with explainability constraints
"Explainable intrusion detection system connected vehicles CAN bus real-time" — to find papers on XAI-IDS specifically designed for automotive network protocols.
"SHAP LIME feature importance vehicular network anomaly detection edge computing" — to find work on explainability methods applied to vehicle cybersecurity at the edge.
"V2X cybersecurity machine learning computational efficiency explainability trade-off" — to find papers addressing the accuracy-efficiency-explainability trilemma in Vehicle-to-Everything communication security.
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