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An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks

Kanwal Rashid¹, Yousaf Saeed¹, Abid Ali^2,3

¹Department of IT, The University of Haripur, Haripur 22620, Pakistan.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This summary is machine-generated.

This study introduces a real-time machine learning system for detecting malicious nodes and Distributed Denial of Service (DDoS) attacks in Vehicular Ad Hoc Networks (VANETs), achieving 99% accuracy.

Keywords:

DDoS OMNET++VANET machine learning real-time malicious nodes

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Area of Science:

Computer Science
Network Security
Artificial Intelligence

Background:

Vehicular Ad Hoc Networks (VANETs) face significant security challenges, particularly from malicious nodes launching Distributed Denial of Service (DDoS) attacks.
Existing solutions often fail to address real-time malicious node detection effectively within VANETs.
DDoS attacks disrupt vehicle communication by overwhelming targeted vehicles with traffic, preventing packet reception and response.

Purpose of the Study:

To propose and evaluate a real-time malicious node detection system for VANETs using machine learning.
To enhance the security mechanisms within VANETs against sophisticated cyber threats.
To address the limitations of current solutions in real-time attack scenarios.

Main Methods:

Development of a distributed multi-layer classifier for real-time malicious node detection.
Utilizing machine learning models including Gradient Boosting Trees (GBT), Logistic Regression (LR), Multi-layer Perceptron Classifier (MLPC), Random Forest (RF), and Support Vector Machines (SVM).
Evaluation using OMNET++ and SUMO network simulators with a dataset comprising normal and attacking vehicles, leveraging Amazon Web Services for improved network performance.

Main Results:

The proposed system achieved a high attack classification accuracy of 99%.
Specific models demonstrated strong performance: SVM (97%), RF (98%), GBT (97%), and LR (94%).
Adoption of Amazon Web Services improved network performance, maintaining efficiency with increased node counts.

Conclusions:

The developed machine learning-based system effectively detects malicious nodes and DDoS attacks in real-time within VANETs.
The distributed multi-layer classifier offers a robust and scalable solution for enhancing VANET security.
The integration with cloud infrastructure ensures efficient performance and adaptability to growing network sizes.