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Comprehensive Host-Based Malicious Behaviour Detection in VANETs
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AI
DOI：10.1007/s10922-026-10067-0.png)
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Abstract
En 中文
Given the safety-critical nature of Vehicular Ad Hoc Networks (VANETs), ensuring real-time security requires both prompt detection of attacks and accurate identification of the malicious nodes involved. In this context, we introduce ADVENT (Attack/Anomaly Detection in VANETs), a comprehensive system that addresses both tasks simultaneously, bridging a critical gap in prior work, which often treats these components in isolation. Through its use of Federated Learning (FL), ADVENT also addresses privacy concerns for data coming from each node, a requirement that is often overlooked in prior work. To the best of our knowledge, ADVENT is among the first frameworks to provide a holistic integration of these four critical security dimensions in a single real-time architecture. A key strength of ADVENT lies in its lightweight feature engineering module, which reduces computational complexity to $$\mathcal {O}(n)$$ using a single operation strategy, unlike the multi-step and resource-intensive approaches commonly found in related systems. In addition, the federated design of ADVENT minimizes communication overhead and protects sensitive data, improving its applicability in real-world VANET environments.
Keywords:
VANET
Anomaly detection
Malicious behaviour detection
Federated learning
AI/ML
Security
Privacy
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