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An entropy and machine learning based approach for DDoS attacks detection in software defined networks.

Amany I Hassan¹, Eman Abd El Reheem², Shawkat K Guirguis²

¹Institute of Graduate Studies and Research, Alexandria, Egypt. igsr.amany_ibrahim@alexu.edu.eg.

Scientific Reports

|August 5, 2024

Summary

This summary is machine-generated.

This study introduces a hybrid method combining statistical analysis and machine learning to detect and mitigate Distributed Denial of Service (DDoS) attacks in Software-Defined Networks (SDNs). The approach effectively identifies and blocks rapid attacks, enhancing SDN security.

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Area of Science:

Computer Science
Network Security
Cybersecurity

Background:

Software-Defined Networks (SDNs) offer efficient network management but face significant Distributed Denial of Service (DDoS) threats.
Existing DDoS detection methods struggle with evolving attack vectors in SDN environments.

Purpose of the Study:

To propose and evaluate a novel hybrid approach for detecting and mitigating DDoS attacks in SDN environments.
To enhance the security and availability of SDN infrastructure against sophisticated cyber threats.

Main Methods:

A hybrid detection system combining statistical entropy-based analysis with machine learning (k-means clustering).
The system analyzes user impact on system entropy to identify anomalous activities.
Experimental validation using CIC-IDS2017, CSE-CIC-2018, and CICIDS2019 datasets.

Main Results:

The proposed hybrid approach demonstrated high effectiveness in detecting and blocking sudden and rapid DDoS attacks.
The combination of statistical and machine learning methods improved attack identification accuracy.
Significant enhancement in SDN security against DDoS threats was observed.

Conclusions:

The novel hybrid approach offers a robust solution for DDoS attack detection and mitigation in SDNs.
This method has the potential to substantially improve the resilience of SDN environments against cyberattacks.
Further research can explore advanced machine learning algorithms for enhanced performance.