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A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs.

Amalia Amalia1, Yushintia Pramitarini1, Ridho Hendra Yoga Perdana1

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This study introduces a deep learning-based secure routing protocol for vehicle ad hoc networks to prevent blackhole attacks. The proposed method enhances network security and efficiency by identifying malicious nodes and optimizing routing decisions.

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Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Vehicle ad hoc networks (VANETs) are crucial for intelligent transportation systems (ITS), improving traffic flow and safety.
  • VANETs face significant security challenges, particularly blackhole attacks that disrupt communication.
  • Existing security protocols often struggle to effectively mitigate these threats in dynamic network environments.

Purpose of the Study:

  • To propose a novel deep-learning-based secure routing (DLSR) protocol for VANETs.
  • To enhance network security against blackhole attacks using deep learning techniques.
  • To improve connectivity and reduce control overhead through a deep-learning-based clustering (DLC) protocol.

Main Methods:

  • Developed a DLSR protocol employing deep learning (DL) for secure route selection and malicious node identification.
  • Implemented a DLC protocol as an underlying structure to boost node connectivity and decrease control overhead.
  • Designed a deep neural network (DNN) model to optimize fitness functions in both DLSR and DLC protocols, considering parameters like energy, distance, and hop count.

Main Results:

  • The DLSR protocol effectively identifies malicious nodes and selects secure routes based on a fitness function.
  • The DLC protocol enhances network connectivity and reduces control overhead.
  • Performance evaluation demonstrated improvements in packet delivery ratio, reduced routing delay, and lower packet loss.

Conclusions:

  • The proposed DLSR and DLC protocols offer a robust solution for securing VANETs against blackhole attacks.
  • Deep learning integration significantly enhances the security and efficiency of intelligent transportation systems.
  • The study highlights the effectiveness of the proposed protocols under different mobility models (RPGM, RWP).