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The Intersection of Machine Learning and Wireless Sensor Network Security for Cyber-Attack Detection: A Detailed

Tahesin Samira Delwar1, Unal Aras1, Sayak Mukhopadhyay2

  • 1Department of Smart Robot Convergence and Application Engineering, Pukyong National University, Busan 48513, Republic of Korea.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary

Machine learning (ML) enhances Wireless Sensor Network (WSN) security by addressing challenges like anomaly detection and congestion. This study reviews ML

Keywords:
Path Planning (PP)Quality of Service (QoS)Sensor Node Deployment (SND)Wireless Sensor Networks (WSNs)machine learning (ML)

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Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Wireless Sensor Networks (WSNs) are vital but face unique security challenges due to inherent constraints.
  • Existing security measures in WSNs are often insufficient to counter sophisticated threats.

Purpose of the Study:

  • To examine the integration of machine learning (ML) for enhancing WSN security.
  • To identify the advantages and disadvantages of various ML algorithms applied to WSN security.
  • To highlight key challenges in WSN security, including localization, coverage, anomaly detection, congestion control, and Quality of Service (QoS).

Main Methods:

  • Comprehensive literature review of existing experimental studies on ML applications in WSN security.
  • Analysis of the effectiveness and limitations of different ML algorithms in addressing WSN security issues.

Main Results:

  • ML offers significant potential for improving WSN security across various critical areas.
  • Specific ML algorithms show promise in mitigating challenges such as anomaly detection and congestion control.
  • The study identifies areas requiring further innovation to fully leverage ML in WSN security.

Conclusions:

  • Machine learning is a powerful tool for bolstering the security and reliability of Wireless Sensor Networks.
  • Addressing WSN security nuances with ML is crucial for maintaining network integrity in interconnected environments.
  • Further research and development are needed to optimize ML-driven security solutions for WSNs.