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Related Concept Videos

Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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SCADA intrusion detection using deep factorization machines.

Mohammed Zakariah1, Syed Umar Amin2, Fatma S Alrayes3

  • 1Department of Computer Science and Engineering, College of Applied Studies, King Saud University, P.O. Box 22459, 11495, Riyadh, Saudi Arabia.

Scientific Reports
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Deep Factorization Machine (DeepFM) for detecting cyberattacks in Supervisory Control and Data Acquisition (SCADA) systems within Industrial Internet of Things (IIoT) environments. The DeepFM method significantly enhances intrusion detection accuracy and performance compared to traditional techniques.

Keywords:
Deep factorization machineIntrusion attacksIntrusion detection systemMachine learningSCADA

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Area of Science:

  • Cybersecurity
  • Industrial Control Systems
  • Machine Learning

Background:

  • Supervisory Control and Data Acquisition (SCADA) systems integrated into Industrial Internet of Things (IIoT) are vulnerable to sophisticated cyberattacks.
  • Traditional intrusion detection methods struggle with the high dimensionality and complex patterns of IIoT cyber threats.
  • Existing techniques like signature-based, statistical anomaly-based, and classical machine learning are insufficient for modern IIoT security challenges.

Purpose of the Study:

  • To propose a novel Deep Factorization Machine (DeepFM) based intrusion detection scheme tailored for SCADA systems in IIoT environments.
  • To leverage DeepFM's ability to model both low-order feature interactions and high-order representations for improved attack detection.
  • To evaluate the effectiveness and generalizability of the proposed DeepFM scheme across diverse SCADA datasets.

Main Methods:

  • Development of a Deep Factorization Machine (DeepFM) framework combining factorization machines and deep neural networks.
  • Integration of DeepFM for modeling complex feature interactions in SCADA network traffic.
  • Testing and validation of the DeepFM scheme on four benchmark datasets: WUSTL-IIoT-2018, WUSTL-IIoT-2021, HAI Security, and Sherlock.

Main Results:

  • DeepFM achieved near-perfect accuracy (99.98%) and an F1-score (0.9997) on the WUSTL-IIoT-2018 dataset.
  • High performance was observed on WUSTL-IIoT-2021 (98.72% accuracy, 0.9945 F1-score) and HAI (95.6% accuracy, 0.967 precision, 0.973 recall).
  • The model demonstrated robust performance on the Sherlock dataset with 95.4% accuracy and a 0.955 F1-score, outperforming conventional methods.

Conclusions:

  • The DeepFM-based intrusion detection scheme is highly effective and accurate for SCADA systems in IIoT environments.
  • DeepFM demonstrates flexibility, resilience, and scalability, making it suitable for a wide range of industrial systems.
  • The proposed method offers a practical and superior alternative to traditional approaches for enhancing IIoT security.