Enterprise AI Analysis
Mitigating Distributed Denial-of-Service Cyberattacks with Deep Reinforcement Learning in Federated Computing
Authored by: Louai A. Maghrabi, Mahmoud Ragab, Bandar Alghamdi, Almuhannad S. Alorfi, Diaa Hamed, Amal Alharbi & Abdullah AL-Malaise Al-Ghamdi
Scientific Reports | (2025) 15:40197 | Published online: 17 November 2025
Executive Impact
This research introduces MDDoSFL-DRLFLO, a groundbreaking technique that leverages federated learning and deep reinforcement learning to provide superior protection against distributed denial-of-service (DDoS) cyberattacks. It offers a collaborative, efficient, and highly accurate solution for identifying and classifying threats in modern computing frameworks.
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Fastest Detection Time (ToN-IoT)
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Key Challenges Addressed
The proliferation of sophisticated DDoS attacks poses a continuous and significant threat to cybersecurity, leading to service disruption and economic losses. Existing machine learning solutions often struggle with the dynamic nature of these attacks, requiring large centralized datasets and lacking real-time adaptability or robust classification performance across diverse environments. This paper directly confronts these challenges by providing an intelligent, collaborative, and highly efficient defense mechanism.
Unique Value Proposition for Enterprise
MDDoSFL-DRLFLO offers enterprises a robust, privacy-preserving, and highly scalable solution for DDoS threat mitigation. Its federated learning framework enables collaborative model training across distributed systems without sharing sensitive raw data, a critical advantage for data privacy and compliance. The integration of advanced optimization and deep reinforcement learning ensures superior accuracy and rapid threat detection, leading to enhanced operational resilience and reduced vulnerability to cyberattacks.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The MDDoSFL-DRLFLO technique demonstrated a superior accuracy of 99.52% in detecting and classifying DDoS attacks across the CICIDIS 2017 and ToN-IoT datasets, significantly outperforming existing methods. This high accuracy ensures robust protection against cyber threats.
99.52%
Accuracy in DDoS Detection
The MDDoSFL-DRLFLO methodology integrates several advanced techniques: z-score normalization for data standardization, an improved Bacterial Foraging Optimization Algorithm (IBFOA) for efficient feature selection, a Dueling Double Deep Q-Network (D3QN) for robust classification, and Frilled Lizard Optimization (FLO) for optimal hyperparameter tuning.
Enterprise Process Flow
Data Normalization (z-score)
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Feature Selection (IBFOA)
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D3QN-based Classification
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Hyperparameter Tuning (FLO)
MDDoSFL-DRLFLO consistently outperforms traditional methods across key metrics. It achieves superior accuracy and significantly faster computational times, attributed to its optimized feature selection and advanced DRL classification capabilities. Traditional methods often suffer from lower accuracy, slower processing, and less robust handling of complex intrusion patterns.
| Feature | MDDoSFL-DRLFLO Advantages | Traditional Methods Limitations |
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The MDDoSFL-DRLFLO framework's application to real-world IoT datasets (ToN-IoT) showcases its capability to provide robust, real-time DDoS threat mitigation for critical infrastructure. Its collaborative FL approach, combined with optimized feature selection and DRL classification, ensures high accuracy and rapid response, crucial for maintaining operational continuity in vulnerable IoT environments.
Case Study: Securing Critical IoT Infrastructure
Problem: IoT environments are highly vulnerable to sophisticated DDoS attacks, leading to service disruption and data breaches. Existing security solutions often lack the adaptability and speed required for real-time threat detection in distributed systems.
Solution: The MDDoSFL-DRLFLO framework was applied to the ToN-IoT dataset, which simulates real-world IoT traffic including various attack types. Its federated learning approach allowed for collaborative model training on distributed data without compromising privacy. The integrated IBFOA and D3QN enabled rapid and accurate identification of malicious traffic.
Outcome: The system achieved an outstanding 99.51% average accuracy on the ToN-IoT testing phase, along with a computational time of just 6.60 seconds. This demonstrates MDDoSFL-DRLFLO's effectiveness in providing robust, real-time DDoS threat mitigation for critical IoT infrastructure, ensuring operational continuity and data integrity.
Advanced ROI Calculator
Estimate the potential savings and reclaimed productivity hours your enterprise could achieve by implementing an advanced AI-driven cybersecurity solution like MDDoSFL-DRLFLO.
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Your AI Implementation Roadmap
A phased approach to integrating the MDDoSFL-DRLFLO solution into your enterprise, ensuring a smooth transition and maximizing impact.
Phase 1: Data Preprocessing & Feature Engineering (2-4 Weeks)
Establish secure federated data pipelines, implement z-score normalization, and apply IBFOA for optimal feature selection on distributed datasets, ensuring data quality and relevance.
Phase 2: DRL Model Training & Optimization (4-6 Weeks)
Deploy and train D3QN models across federated nodes, focusing on collaborative learning and initial attack pattern recognition. This phase builds the core intelligence for threat detection.
Phase 3: Hyperparameter Tuning & Validation (3-5 Weeks)
Utilize FLO to fine-tune D3QN hyperparameters, rigorously validate model performance on diverse attack scenarios (CICIDIS 2017, ToN-IoT), and ensure generalization across different threat types.
Phase 4: Integration & Real-time Deployment (4-8 Weeks)
Integrate the MDDoSFL-DRLFLO system into existing cybersecurity infrastructure, enable real-time threat detection and classification, and establish continuous monitoring for adaptive defense against evolving threats.
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