An Intelligent Intrusion Detection and Adaptive Analytics Architecture for Strengthening Situational Awareness in VANET Security and Attack Mitigation Systems
Keywords:
VANET security, intrusion detection system, situational awareness, adaptive analyticsAbstract
Vehicular Ad-Hoc Networks (VANETs) form the backbone of intelligent transportation systems by enabling real-time communication among vehicles and roadside infrastructure. However, the open and highly dynamic nature of VANET environments makes them particularly vulnerable to a wide range of cyberattacks, including spoofing, denial-of-service (DoS), and message manipulation attacks. These threats severely degrade situational awareness, disrupt decision-making processes, and compromise road safety. This research proposes an intelligent intrusion detection and adaptive analytics architecture designed to strengthen situational awareness in VANET security ecosystems. The proposed framework integrates machine learning-based anomaly detection, context-aware data fusion, and adaptive trust evaluation mechanisms to enhance attack detection accuracy and response efficiency.
The study builds upon established research in vehicular security, trust management, and situational awareness modeling. Prior works emphasize the importance of secure communication protocols (Bagga et al., 2020), trust-aware VANET architectures (Sateesh and Zavarsky, 2020), and intrusion prevention mechanisms for distributed vehicular systems (Poongodi et al., 2019). However, existing solutions often lack adaptive intelligence and fail to incorporate real-time situational awareness modeling across dynamic vehicular environments. Addressing this gap, the proposed architecture introduces an adaptive analytics layer that continuously refines detection parameters based on contextual driving conditions and behavioral patterns.
Furthermore, the research integrates cognitive models of situational awareness in autonomous systems, particularly emphasizing remote monitoring and operator perception challenges (Mutzenich et al., 2021). The findings suggest that enhancing situational awareness through intelligent analytics significantly improves intrusion detection precision while reducing false-positive rates. The proposed system demonstrates a scalable and resilient framework suitable for next-generation intelligent transportation infrastructures.
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