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Updated: Sep 17, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative

Mohammed Aly1, Mohamed H Behiry2,3

  • 1Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, Badr, 11829, Egypt. mohammed-alysalem@eru.edu.eg.

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Summary

Logistic Boosting effectively detects anomalies in industrial IoT settings, outperforming Random Forest and SVM. This machine learning approach offers high accuracy for real-time industrial anomaly detection systems.

Keywords:
Anomaly detectionImbalanced dataIndustrial cybersecurityIoTLogistic boostingMachine learningROC analysis

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Area of Science:

  • Industrial IoT
  • Machine Learning
  • Anomaly Detection

Background:

  • Industrial environments generate vast sensor data.
  • Effective anomaly detection is crucial for operational efficiency and safety.
  • Existing machine learning models require evaluation for industrial IoT anomaly detection.

Purpose of the Study:

  • To evaluate and compare Logistic Boosting, Random Forest, and Support Vector Machines (SVM) for anomaly detection in industrial IoT.
  • To identify the most effective machine learning algorithm for classifying anomalies in factory sensor data.
  • To provide insights for developing robust real-time anomaly detection systems.

Main Methods:

  • Analysis of a real-world dataset comprising 15,000 instances from factory sensors.
  • Utilized Receiver Operating Characteristic (ROC) curves, confusion matrices, and standard performance metrics.
  • Comparative evaluation of Logistic Boosting, Random Forest, and Support Vector Machines (SVM) algorithms.

Main Results:

  • Logistic Boosting achieved the highest performance with an Area Under the Curve (AUC) of 0.992, 96.6% accuracy, 93.5% precision, 94.8% recall, and an F1-score of 0.941.
  • Logistic Boosting demonstrated superior handling of imbalanced data, with 134 false positives and 117 false negatives.
  • Random Forest showed strong results (AUC = 0.9982), and SVM exhibited high recall, but Logistic Boosting's ensemble method was most effective.

Conclusions:

  • Logistic Boosting is the most effective machine learning algorithm for anomaly detection in industrial IoT environments.
  • The findings support the implementation of Logistic Boosting for real-time anomaly detection systems in factories.
  • Future research should explore hybrid architectures and edge optimization for enhanced industrial IoT anomaly detection.