Applying Machine Learning for Anomaly Detection in Wireless Sensor Networks

References

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@article{1710586,
      author = {Veena V, Nikitha B, Prachi kachhap, Sandhya A K, Deepti N N},
      title = {Applying Machine Learning for Anomaly Detection in Wireless Sensor Networks},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {3},
      pages = {652-655},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710586.pdf},
      abstract = {Wireless Sensor Networks (WSNs) have become integral to a wide range of applications, including environmental monitoring, industrial automation, and smart cities. However, their distributed and resource-constrained nature makes them particularly vulnerable to anomalies arising from hardware malfunctions, communication failures, or security attacks. Accurate and timely anomaly detection is crucial to maintain the reliability, security, and performance of these networks. In recent years, machine learning (ML) techniques have emerged as powerful tools to enhance anomaly detection capabilities in WSNs by enabling systems to learn complex patterns of normal behavior and identify deviations indicative of anomalies. This report explores the application of various machine learning models for anomaly detection in WSNs. We provide a comprehensive overview of supervised, unsupervised, and semi-supervised learning approaches, highlighting their suitability for different types of WSN data and deployment scenarios. Techniques such as k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Decision Trees, Isolation Forests, Autoencoders, and clustering algorithms like k-Means and DBSCAN are examined for their performance in detecting both point anomalies and contextual anomalies.},
      keywords = {Wireless Sensor Networks (WSN), Anomaly Detection, Machine Learning, Outlier Detection, Intrusion Detection, Fault Detection, Energy Efficiency. Supervised Learning, Unsupervised Learning, Classification, Clustering, SVM, KNN, Random Forest, Neural Networks.},
      month = {September},
  }