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Development and Analysis of a Better AODV Routing Protocol for Improved Network Performance in Manets Using a Machine Learning Model

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
V. Narasimha
C.H. Rajakishore Babu
Golla Saidulu
K. Srujan Raju
Mohan Babu Bukya
Kotha Chandrakala
Published in
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering
Publisher
Springer Nature Singapore

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Abstract

This chapter explores the development and analysis of an improved AODV routing protocol for Mobile Ad Hoc Networks (MANETs) using machine learning models. The study focuses on detecting and mitigating black hole attacks, which are particularly harmful to MANETs. By employing Support Vector Machines (SVMs), the research aims to enhance the performance and security of the network. The methodology involves network simulation using NS-2.38, data collection, and model training and testing. The results demonstrate an 82.55% accuracy in detecting malicious nodes, significantly improving network performance and reducing packet loss. The study also highlights the potential of machine learning in optimizing routing decisions, predicting link stability, and controlling energy consumption in MANETs. The findings suggest that the proposed method can effectively segregate malicious nodes, thereby improving the overall performance of Intrusion Detection Systems (IDS) in MANETs.
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previous chapter Implementing Supervised Machine Learning Algorithms on an Investigation of Customer Mobility in the Telecom Sector
next chapter A Review on Early Prediction of Heart Failure Using Machine Learning Models
Title
Development and Analysis of a Better AODV Routing Protocol for Improved Network Performance in Manets Using a Machine Learning Model
Authors
V. Narasimha
C.H. Rajakishore Babu
Golla Saidulu
K. Srujan Raju
Mohan Babu Bukya
Kotha Chandrakala
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering

Print ISBN: 978-981-9502-68-4

Electronic ISBN: 978-981-9502-69-1

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-0269-1_5
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