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Related Experiment Videos

Enhancing IoT security: A Creative Swagger Optimization algorithm for DDoS defence.

Rahul Rajendra Papalkar1, Abrar S Alvi2

  • 1Department of Information Technology, Prof. Ram Meghe Institute of Technology & Research, Badnera-Amravati, India.

Network (Bristol, England)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Creative Swagger (CS) Optimized Deep Convolutional Neural Network (DeepCNN) to detect and mitigate Distributed Denial of Service (DDoS) attacks in the Internet of Things (IoT). The novel CS-DeepCNN model achieves high accuracy, offering a robust solution for IoT security.

Keywords:
CloudCreative Swagger OptimizationDistributed denial of serviceconvolutional neural networkinternet of things

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • Internet of Things (IoT) systems are vulnerable to security threats due to extensive data exchange.
  • Distributed Denial of Service (DDoS) attacks compromise IoT server availability by overwhelming them with traffic.
  • Existing DDoS detection methods require enhancement for improved accuracy and robustness in IoT environments.

Purpose of the Study:

  • To propose a novel method for detecting and mitigating DDoS attacks in IoT networks.
  • To enhance the accuracy and robustness of DDoS attack detection using optimized deep learning models.
  • To introduce the Creative Swagger (CS) algorithm for optimizing Deep Convolutional Neural Network (DeepCNN) parameters.

Main Methods:

  • Development of a Creative Swagger (CS) algorithm by integrating Swagger behavior with innovative concepts.
  • Optimization of Deep Convolutional Neural Network (DeepCNN) parameters using the CS algorithm for improved DDoS detection.
  • Implementation of a blacklist table for initial verification of network traffic, including IP address checks.
  • Training and validation of the CS-optimized DeepCNN model on the UNSW-NB15 Dataset.

Main Results:

  • The CS-optimized DeepCNN model achieved a detection accuracy of 97.07%.
  • The model demonstrated high sensitivity (97.23%) and specificity (96.91%) at 80% training data.
  • The proposed method showed significant robustness in detecting DDoS attacks on IoT platforms.

Conclusions:

  • The CS-optimized DeepCNN presents an effective solution for detecting and mitigating DDoS attacks in IoT environments.
  • The fusion of the CS algorithm with DeepCNN significantly enhances detection accuracy and model robustness.
  • This research contributes a more secure and reliable approach to safeguarding IoT network communications.