An Intelligent Latency Aware DDoS Detection Framework for Secure Vehicular Ad Hoc Networks

ABSTRACT Vehicular ad hoc networks (VANETs) enable real‐time communication but are vulnerable to security threats, particularly distributed denial of service (DDoS) attacks, that cause delays and network failures. Traditional static detection systems struggle to adapt to dynamic traffic conditions. To address this problem, we propose ELITE, a lightweight and intelligent DDoS detection framework designed for secure VANETs. ELITE employs a three‐layer architecture featuring a random fuzzy tree (RFT) classifier, which combines the speed of decision trees with adaptive fuzzy reasoning for efficient anomaly detection. It also includes a latency‐aware scheduling system that ensures the urgent traffic is handled, while a few essential requests are sent to nearby edge servers or to the cloud. This work has three distinct contributions: integration of X and Y into one intelligent smart‐environment architecture, like edge‐cloud optimization model 96% stability of delay‐sensitive edge‐cloud optimization, and development of a lightweight threat detection module with improved accuracy and real‐time capability. Experimental results demonstrate that ELITE achieves a high detection accuracy of 95.7%, effectively adapts to traffic changes, reduces false positives, and improves latency performance.