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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

Summary

This summary is machine-generated.

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.