Sybil Attacks Detection and Traceability Mechanism Based on Beacon Packets in Connected Automobile Vehicles

Area of Science:

• Cybersecurity
• Intelligent Transportation Systems

Background:

• Connected Automobile Vehicles (CAVs) facilitate cooperative driving but are vulnerable to Sybil attacks where malicious vehicles forge identities.
• Existing defenses focus on detection, neglecting traceability, thus failing to address the root cause of Sybil attacks.

Purpose of the Study:

• To develop a novel mechanism for tracing the source of malicious vehicles in CAV networks.
• To enhance the security and reliability of cooperative driving by addressing Sybil attacks effectively.

Main Methods:

• Utilizing roadside units (RSUs) to instruct vehicles on customized key broadcasting and listening.
• Constructing a neighbor graph based on analyzed beacon packets to assess vehicle credibility.
• Determining vehicle credibility by calculating edge success probability within the neighbor graph.

Main Results:

• Achieved real-time detection and tracking of Sybil vehicles.
• Demonstrated high precision (98.53%) and recall (95.93%) in identifying malicious vehicles.
• Successfully addressed the limitations of existing detection schemes by enabling traceability.

Conclusions:

• The proposed scheme effectively detects and traces Sybil vehicles, offering a fundamental solution to Sybil attacks in CAVs.
• This approach enhances the security of connected vehicle networks by identifying and locating malicious entities.