Fuzzy Ranking for Enhanced Security: Optimizing Feature Selection in VANET Intrusion Detection

In Vehicular Ad-Hoc Networks (VANETs), ensuring efficient intrusion detection while minimizing false alarms is paramount for maintaining network security and reliability. Traditional methods often struggle to strike this balance effectively. This study proposes a novel approach that employs fuzzy ranking to enhance intrusion detection efficiency in VANETs while mitigating false positive and negative factors. At the core of our approach lies the development of an innovative fuzzy ranking mechanism to identify optimal features extracted from VANET nodes. These features serve as critical indicators of potential security threats within the network. By systematically collecting and analyzing data from various sources including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, our approach aims to capture nuanced patterns and anomalies indicative of malicious behavior. The fuzzy ranking technique enables us to prioritize features based on their relevance to intrusion detection, thereby facilitating more effective feature selection. This process ensures that only the most informative features are utilized, enhancing the overall efficiency of the intrusion detection system. Features such as packet transmission rates, signal strength variations, route deviation patterns, and network topology characteristics are carefully evaluated and ranked to optimize detection accuracy. To train our intrusion detection model, we leverage machine learning algorithms such as deep neural networks, support vector machines, and decision trees. By utilizing labeled datasets containing examples of both benign and malicious network activities, our model learns to distinguish between normal and anomalous behavior, thus enabling timely threat detection. Furthermore, we integrate fuzzy logic-based anomaly detection techniques to identify previously unseen or zero-day attacks, enhancing the system's robustness. In addition to improving detection accuracy, our approach focuses on preventing harmful warnings caused by false alarms. We achieve this by implementing a comprehensive filtering mechanism that distinguishes between transient anomalies and genuine security threats. Contextual factors such as traffic conditions, environmental variables, and historical network behavior are considered to reduce false positives while ensuring reliable threat detection.Through extensive experimentation and evaluation, we demonstrate the effectiveness of our approach in enhancing intrusion detection efficiency and reducing false positive/negative factors in VANETs. The proposed methodology offers a promising solution to the security challenges faced by VANETs, paving the way for safer and more secure vehicular communication systems in the future.