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Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

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Published on: November 18, 2019

Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine

Himanshi Babbar¹, Shalli Rani¹, Maha Driss^2,3

¹Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India.

|December 18, 2024

Summary

This summary is machine-generated.

This study introduces new methods for detecting Distributed Denial of Service (DDoS) attacks in Software-Defined Vehicular Networks (SDVN) using Machine Learning (ML). The Random Forest model demonstrated superior performance in identifying malicious traffic.

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Area of Science:

Cybersecurity
Network Engineering
Artificial Intelligence

Background:

Vehicular Networks (VN) are crucial for traffic optimization and safety.
Software-Defined Networking (SDN) enhances wireless network capabilities.
VN are increasingly vulnerable to Distributed Denial of Service (DDoS) attacks.

Purpose of the Study:

To propose novel methodologies for detecting DDoS attacks in Software-Defined Vehicular Networks (SDVN).
To implement Machine Learning (ML) algorithms within SDN Intrusion Detection Systems (IDS) for vehicular environments.
To address challenges of imbalanced datasets and distinguish between different attack types.

Main Methods:

Statistical flow analysis and entropy computation.
Implementation of ML algorithms (K-nearest Neighbor, Random Forest, Logistic Regression) on the BoT-IoT dataset.
Feature subset selection to optimize model accuracy and evaluate dataset attribute impact.

Main Results:

The Random Forest classifier achieved high performance metrics: 92% Precision, 92% F1-score, 91% Accuracy, and 90% Recall over five iterations.
The study identified optimal sample sizes and evaluated dataset attribute impacts on performance.
The proposed methodology effectively distinguishes between reconnaissance, DoS, and DDoS traffic.

Conclusions:

Machine Learning, particularly Random Forest, is effective for DDoS detection in SDVN.
Efficient data handling and potential edge computing are crucial for real-time performance.
The developed approach offers a scalable solution for enhancing vehicular network security.