A Fuzzy-Based Context-Aware Misbehavior Detecting Scheme for Detecting Rogue Nodes in Vehicular Ad Hoc Network

Area of Science:

• Vehicular Ad Hoc Networks (VANETs)
• Network Security
• Fuzzy Logic Systems

Background:

• VANETs enhance road safety and traffic efficiency through vehicle communication.
• Rogue nodes sharing false data pose significant risks to VANET applications.
• Existing detection methods struggle with dynamic data and uncertainty, leading to poor performance.

Purpose of the Study:

• To develop a fuzzy-based context-aware detection model for identifying rogue nodes in VANETs.
• To improve the accuracy and performance of rogue node detection compared to current solutions.
• To enhance the overall safety and efficiency of vehicular networks.

Main Methods:

• A fuzzy inference system was designed to evaluate vehicle-generated information.
• A dynamic context reference was established using the fuzzy inference system's output.
• Vehicles were classified as honest or rogue based on their evaluation score deviation from the context reference.

Main Results:

• The proposed fuzzy-based model significantly outperformed state-of-the-art models.
• Achieved a 7.88% improvement in overall performance.
• Attained a 16.46% improvement in detection rate for rogue nodes.

Conclusions:

• The fuzzy-based context-aware model effectively detects rogue nodes in VANETs.
• The model enhances network safety and traffic efficiency for various vehicle types and environments.
• This approach offers a robust solution for securing vehicular communication against malicious actors.