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Optimization-assisted deep two-layer framework for ddos attack detection and proposed mitigation in software defined

Karthika Perumal¹, Karmel Arockiasamy¹

¹School of Computer Science and Engineering, Vellore Institute of Technology - Chennai, Chennai, India.

Network (Bristol, England)

|February 13, 2025

Summary

This study introduces a new Software-Defined Networking (SDN) design to combat distributed denial-of-service (DDoS) attacks in Internet of Things (IoT) environments. The system effectively detects and mitigates DDoS threats, enhancing IoT security.

Keywords:

DDoS MUAE Optimized QDNN SDN deep two-layer classifier scheme

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Area of Science:

Computer Science
Network Security
Cybersecurity

Background:

Internet of Things (IoT) devices are increasingly vulnerable to security threats, as evidenced by large-scale distributed denial-of-service (DDoS) attacks.
The proliferation of IoT applications necessitates robust security measures for device management.
The Software-Defined Anything (SDx) paradigm offers a promising approach for secure IoT device management.

Purpose of the Study:

To design and implement a five-phase Software-Defined Networking (SDN) system for detecting and mitigating DDoS attacks in IoT networks.
To enhance the security posture of IoT ecosystems through advanced threat detection and response mechanisms.

Main Methods:

A five-phase SDN architecture incorporating a DDoS attack detection and mitigation system.
Data pre-processing using data cleaning and feature selection via the augmented chi-square method.
A deep two-layer architecture with four classifiers for attack detection.
Optimization of a Quantum Neural Network (QNN) using the hybrid MUAE approach.
A phased approach where normal data routing transitions to attack mitigation upon detection.

Main Results:

The proposed system achieved high accuracy rates, with predictions reaching 96.02% for training rates of 90%.
The system demonstrated superior performance in both DDoS attack detection and mitigation compared to traditional methods.
The optimized QNN effectively identifies malicious activity, triggering the mitigation phase.

Conclusions:

The developed SDN-based system provides an effective solution for securing IoT environments against DDoS attacks.
The integration of advanced machine learning techniques like QNN and optimization methods like MUAE significantly improves threat detection and mitigation capabilities.
The study validates the superiority of the proposed system over conventional approaches in enhancing IoT security.