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A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

Mitigating distributed denial of service-based cyberattack in federated computing framework using deep reinforcement

Louai A Maghrabi¹, Mahmoud Ragab², Bandar Alghamdi³

¹Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia.

Scientific Reports

|November 17, 2025

Summary

This summary is machine-generated.

This study introduces a novel method to combat Distributed Denial of Service (DDoS) attacks using Federated Learning (FL) and Deep Reinforcement Learning (DRL). The technique achieves high accuracy in detecting and classifying these cyber threats.

Keywords:

Deep reinforcement learning Distributed denial of service Feature selection Federated learning Frilled lizard optimization

Related Experiment Videos

Last Updated: Jan 11, 2026

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

Area of Science:

Cybersecurity
Artificial Intelligence
Machine Learning

Background:

Distributed Denial of Service (DDoS) attacks pose a significant and persistent threat to internet infrastructure.
Federated Learning (FL) offers a privacy-preserving approach for collaborative model training on distributed data, gaining traction in cybersecurity.
Deep Learning (DL) and Machine Learning (ML) are crucial for detecting malicious network traffic but require substantial, accurate datasets.

Purpose of the Study:

To propose and evaluate a novel technique, Mitigating DDoS attack in Federated Learning Using Deep Reinforcement Learning and Frilled Lizard Optimization (MDDoSFL-DRLFLO), for enhanced DDoS attack detection and classification.
To leverage the collaborative capabilities of FL with advanced DL techniques for real-time threat identification.
To improve the accuracy and efficiency of DDoS attack mitigation strategies in networked environments.

Main Methods:

Data normalization using z-score standardization.
Feature selection employing an improved bacterial foraging optimization algorithm (IBFOA).
Classification using the Dueling Double Deep Q-Network (D3QN) model, with hyperparameter tuning via the frilled lizard optimization (FLO) approach.

Main Results:

The MDDoSFL-DRLFLO technique demonstrated superior performance in recognizing and classifying DDoS attacks.
Experimental validation on CICIDIS 2017 and ToN-IoT datasets showed a high accuracy of 99.52%.
The proposed method outperformed existing techniques across various evaluation metrics.

Conclusions:

The MDDoSFL-DRLFLO model effectively mitigates DDoS attacks by integrating FL, DRL, and optimization algorithms.
The study highlights the potential of FL and DRL in developing robust cybersecurity solutions.
The achieved high accuracy validates the efficacy of the proposed approach for real-world threat detection.