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Related Experiment Videos

Secure federated learning with metaheuristic optimized dimensionality reduction and multi-head attention for DDoS

Adwan A Alanazi1, Ashrf Althbiti2, Sara Abdelwahab Ghorashi3

  • 1Department of Computer Science and Information, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.

Scientific Reports
|September 26, 2025
PubMed
Summary

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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This study introduces a novel Metaheuristic-Driven Dimensionality Reduction for Robust Attack Defense Using Deep Learning Models (MDRRAD-DLM) to combat Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) environments, achieving high accuracy.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Internet of Things (IoT)

Background:

  • Distributed Denial of Service (DDoS) attacks are a significant threat to Internet of Things (IoT) environments, impacting e-commerce, government, and banking systems.
  • Federated Learning (FL) offers a privacy-preserving approach for training deep learning (DL) models on decentralized data, addressing challenges in cybersecurity.
  • Effective real-time detection and mitigation strategies are crucial for securing IoT devices against evolving cyber threats.

Purpose of the Study:

  • To propose an effective method for detecting and mitigating Distributed Denial of Service (DDoS) attacks in real-world Internet of Things (IoT) applications.
  • To enhance the robustness and accuracy of deep learning models in identifying and defending against cyberattacks.
  • To leverage metaheuristic optimization techniques for improved feature selection and parameter tuning in attack defense models.
Keywords:
DDoS defenseData pre-processingDeep learningDimensionality reductionElk herd optimizerIoTReal-world application

Related Experiment Videos

Main Methods:

  • The Metaheuristic-Driven Dimensionality Reduction for Robust Attack Defense Using Deep Learning Models (MDRRAD-DLM) approach was developed.
  • Data preprocessing involved Z-score normalization, followed by feature selection using the Parrot Optimization (PO) technique.
  • Attack classification was performed using a Temporal Convolutional Network with Multi-Head Attention and Bi-directional Gated Recurrent Unit (TCN-MHA-Bi-GRU), with parameters optimized by the Elk Herd Optimizer (EHO).

Main Results:

  • The MDRRAD-DLM approach demonstrated superior performance in detecting DDoS attacks.
  • Experimental validation on NSLKDD and CIC-IDS2017 datasets yielded high accuracy rates of 99.14% and 99.41%, respectively.
  • The proposed method effectively identified significant features and optimized model parameters for robust attack defense.

Conclusions:

  • The MDRRAD-DLM approach provides an effective solution for real-time DDoS attack detection and mitigation in IoT environments.
  • The integration of metaheuristic optimization with deep learning enhances the accuracy and reliability of cybersecurity defenses.
  • This study highlights the potential of advanced machine learning techniques in securing the ever-expanding landscape of Internet of Things devices.