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Enhancing Security in 5G Edge Networks: Predicting Real-Time Zero Trust Attacks Using Machine Learning in SDN

Fiza Ashfaq¹, Muhammad Wasim¹, Mumtaz Ali Shah²

¹Department of Computer Science, UMT Sialkot Campus, KUST, Sialkot 51040, Pakistan.

Sensors (Basel, Switzerland)

|April 28, 2025

Summary

This summary is machine-generated.

This study introduces a novel machine learning approach for real-time detection of Distributed Denial of Service (DDoS) attacks. The proposed system achieves 99% accuracy in identifying these complex cyber threats within one second.

Keywords:

SDN cyber security intrusion detection intrusion prevention machine learning real-time zero trust

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Area of Science:

Cybersecurity
Machine Learning
Network Security

Background:

The internet faces increasing cyber threats, including sophisticated Distributed Denial of Service (DDoS) attacks.
Traditional security systems struggle to detect advanced DoS and DDoS attacks effectively.
Machine learning (ML) shows promise for enhanced attack detection, yet real-time capabilities remain a challenge.

Purpose of the Study:

To develop and evaluate a real-time system for detecting Distributed Denial of Service (DDoS) attacks.
To address the limitations of current security solutions in identifying complex network intrusions.
To leverage machine learning for accurate and rapid identification of cyber threats.

Main Methods:

A simulated network environment was created using Mininet and the POX Controller.
The CICDDoS2019 dataset was utilized for attack identification and classification.
Pre-trained machine learning models analyzed network traffic in real-time within a virtual software-defined network (SDN).

Main Results:

The proposed methodology achieved a 99% accuracy rate in detecting DDoS attacks.
The system demonstrated a rapid detection time, identifying attacks within 1 second.
The model successfully classified and identified various DDoS attack types in the simulated environment.

Conclusions:

The developed machine learning-based system offers a highly accurate and efficient solution for real-time DDoS attack detection.
The integration of ML with SDN provides a robust framework for enhancing network security against advanced threats.
This research contributes to bridging the gap in real-time detection of complex cyberattacks.