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2000
Volume 18, Issue 5
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Introduction

Wireless networks are essential communication technologies that prevent cable installation prices and burdens. Because of this technology's pervasive usage, wireless network safety is a significant problem. Owing to distributed and open wireless medium aspects, attackers might use different jamming methods to exploit physical and MAC layer protocol vulnerabilities. In addition, jamming attacks require to be accurately grouped so that suitable countermeasures can be considered. Given the potential severity of such attacks, precisely identifying and classifying them is critical for implementing effective responses. The motivation for this paper is the need to improve the detection and categorization of jamming signals using modern machine learning algorithms, consequently enhancing wireless network security and reliability.

Objective

In this paper, we compare some machine learning models' efficiency for diagnosing jamming signals.

Methods

Such algorithms refer to support vector machine (SVM) and k-nearest neighbors (KNN). We checked the signal features that recognize jamming signals. After the jamming attack model, the developed grey wolf optimizer version known as IGWO (improved grey wolf optimizer) has been discussed for feature extraction of software usability. Four separate metrics were employed as features to detect jamming attacks in order to evaluate the machine learning models. This novel feature extraction method is crucial for improving the accuracy of jamming detection.

Results

The measurements of these parameters were gathered through a simulation of a real setting. And generated a large dataset using these parameters.

Conclusion

The simulation results illustrate that the KNN algorithm based on jamming detection could diagnose jammers having a minimal likelihood of false alarms and a high level of accuracy.

© 2025 Bentham Science Publishers
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2025-11-02

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Jamming Attacks Detection based on IGWO for Optimization of Fast Correlation-based Feature Extraction in Wireless Communication
Recent Advances in Computer Science and Communications 18, E26662558341873 (2025); https://doi.org/10.2174/0126662558341873241016064323
/content/journals/rascs/10.2174/0126662558341873241016064323
/content/journals/rascs/10.2174/0126662558341873241016064323
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  • Article Type:     Research Article
Keyword(s): classifier; improved grey wolf optimizer (IGWO); jamming attacks; K-nearest neighbors; Machine learning; support vector machine

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