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Secure malicious node detection in flying ad-hoc networks using enhanced AODV algorithm.

V Chandrasekar1, V Shanmugavalli2, T R Mahesh1

  • 1Department of Computer Science & Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, 562112, India.

Scientific Reports
|April 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a secure AODV algorithm to detect and isolate malicious nodes in flying ad hoc networks (FANETs). The proposed method enhances network security and performance by reducing packet loss and routing overhead.

Keywords:
AODVFlying ad-hoc networkMalicious nodeSecure AODVTAODV

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Area of Science:

  • Computer Science
  • Network Security
  • Wireless Communication

Background:

  • Flying ad hoc networks (FANETs) are increasingly used but face significant security challenges due to dynamic node behavior and malicious attacks.
  • Malicious nodes injecting false information destabilize FANETs, degrading network performance and reliability.

Purpose of the Study:

  • To propose and evaluate a novel threat detection method for identifying and isolating malicious nodes in FANETs.
  • To enhance the security and efficiency of FANETs using a secure Ad hoc On-Demand Distance Vector (AODV) algorithm.

Main Methods:

  • Implemented a secure AODV algorithm incorporating trust models based on direct/indirect reliability for node validation.
  • Developed a threat detection mechanism to identify and disconnect malicious nodes from the network.
  • Evaluated performance metrics including throughput, packet loss, and routing overhead against conventional AODV algorithms.

Main Results:

  • The proposed secure AODV algorithm demonstrated superior performance in energy consumption, throughput, packet delivery rate, and reduced packet loss and routing overhead.
  • Achieved a 16.5% decrease in power consumption, a 7.4% increase in efficiency, and a 9.1% reduction in packet delivery rate compared to the second-ranked method.
  • Reported a 9.4% reduction in packet losses and routing expenses.

Conclusions:

  • The developed secure AODV algorithm effectively detects and isolates malicious nodes in FANETs.
  • The proposed method significantly improves FANET security and performance metrics over existing algorithms.
  • This research contributes a robust solution for enhancing the stability and trustworthiness of drone-based wireless networks.