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DLSMR: Deep Learning-Based Secure Multicast Routing Protocol against Wormhole Attack in Flying Ad Hoc Networks with

Yushintia Pramitarini1, Ridho Hendra Yoga Perdana1, Kyusung Shim2

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This study introduces a deep learning secure multicast routing protocol (DLSMR) for flying ad hoc networks (FANETs) using cell-free massive MIMO. The protocol effectively avoids wormhole attacks and enhances network connectivity and stability.

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CF-mMIMOclusteringdeep learningflying ad hoc networkssecure multicast routingsecuritywormhole attack

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Security

Background:

  • Future networks require robust solutions for managing diverse nodes, especially in aerial environments.
  • Cell-free massive multiple-input multiple-output (CF-mMIMO) offers a promising paradigm for enhanced network capacity and coverage.
  • Security and Quality of Service (QoS) are critical parameters for next-generation communication networks.

Purpose of the Study:

  • To propose a novel deep learning-based secure multicast routing protocol (DLSMR) for Flying Ad Hoc Networks (FANETs) integrated with CF-mMIMO.
  • To address and mitigate wormhole attacks within the multicast routing process in FANETs.
  • To enhance network security, scalability, and stability in aerial communication environments.

Main Methods:

  • Developed a deep learning (DL) model within the DLSMR protocol to predict secure routes based on node ID, distance, destination sequence, hop count, and energy.
  • Proposed a top-down particle swarm optimization-based clustering (TD-PSO) protocol to optimize node connectivity and manage multicast members.
  • Evaluated the DLSMR and TD-PSO protocols using performance metrics including packet delivery ratio, routing delay, control overhead, and packet loss ratio.

Main Results:

  • The DLSMR protocol successfully establishes high-stability multicast trees and improves security against wormhole attacks by accurately predicting secure routes.
  • The TD-PSO protocol enhances node connectivity and ensures convergence to global optima, strengthening the overall network structure.
  • Performance evaluations confirmed the proposed protocols' effectiveness in providing secure routing, avoiding wormhole attacks, and ensuring robust connectivity.

Conclusions:

  • The DLSMR protocol offers a significant advancement in securing multicast routing in CF-mMIMO enabled FANETs against sophisticated attacks like wormholes.
  • The integration of TD-PSO clustering further bolsters network performance by improving connectivity and stability.
  • The research highlights the critical role of deep learning and advanced optimization techniques in addressing the complex challenges of future aerial networks.