DDoS Attack Detection

DDoS Attack Detection in VANET for 5G Networks


  •   A fog-based detection algorithm will be developed to identify and differentiate between normal traffic and DDoS attack traffic in VANET systems enabled by 5G technology.
  •   A comprehensive simulation framework will be designed and implemented to evaluate the effectiveness and performance of the proposed fog-based approach under various attack scenarios.
  •   Extensive experiments and evaluations will be conducted to assess the accuracy of the fog-based approach in detecting DDoS attacks, measuring true positive and false positive rates.
  •   The performance of the fog-based detection approach will be analyzed under different traffic conditions, considering varying vehicle densities, traffic patterns, and communication delays.
  •   Energy efficiency of the fog-based detection approach will be studied, optimizing computational and communication overhead to reduce energy consumption while maintaining effective attack detection.
  •   A comparison will be made between the proposed fog-based approach and existing methods for DDoS attack detection in VANET systems, evaluating advantages, limitations, and performance trade-offs.
  •   Practical considerations and challenges of deploying the fog-based detection approach in real-world smart city environments will be discussed, addressing scalability, interoperability, and potential implementation issues.

  • Description

The Intelligent Transport System (ITS) includes VANET as an essential component. VANET is a system that enables vehicle nodes to exchange crucial and potentially life-saving information. It is important to rapidly identify any attacks on the VANET system. DDoS attacks are a type of cyber attack that can impact the availability of VANET systems. The exchange of valuable information between vehicle nodes is currently not possible due to a DDoS attack. The paper proposes a fog-based approach for detecting DDoS attacks in smart cities enabled by 5G technology. The approach aims to distinguish between normal traffic and attack traffic.