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Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack

Pramodh Krishna D1, E Sandhya2, Khaja Shareef Sk3

  • 1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India.

Scientific Reports
|January 4, 2025
PubMed
Summary

This study introduces a hybrid deep learning model for Mobile Ad Hoc Networks (MANETs) to combat flooding attacks. The novel approach enhances network security, improves data delivery, and conserves energy, outperforming traditional methods.

Keywords:
Convolutional neural networkEvolutionary population dynamics techniqueHunger game searchLong short-term memoryMobile Ad Hoc Networks

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Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Mobile Ad Hoc Networks (MANETs) offer decentralized communication but are vulnerable to flooding attacks.
  • Flooding attacks disrupt MANETs by degrading performance and draining energy resources.

Purpose of the Study:

  • To develop a hybrid deep learning model for detecting and mitigating flooding attacks in MANETs.
  • To enhance the security, reliability, and energy efficiency of MANETs.

Main Methods:

  • Integration of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) architectures.
  • Optimization using a novel DECEHGS algorithm (Differential Evolution and Evolutionary Population Dynamics).

Main Results:

  • Achieved 95% accuracy in detecting malicious nodes.
  • Increased packet delivery ratio by 12%.
  • Reduced routing overhead by 20% compared to traditional methods.

Conclusions:

  • The proposed hybrid deep learning model offers an effective and energy-efficient solution for MANET security.
  • Demonstrated significant improvements in network performance and robustness against attacks.