Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Heating and Cooling Curves02:44

Heating and Cooling Curves

23.1K
When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
23.1K
Refrigerators and Heat Pumps01:07

Refrigerators and Heat Pumps

2.4K
Refrigerators or heat pumps are heat engines operating in a reverse direction. For a refrigerator, the focus is on removing heat from a specific area, whereas, for a heat pump, the focus is on dumping heat into one particular area. A refrigerator (or heat pump) absorbs heat Qc from the cold reservoir at Kelvin temperature Tc and discards heat Qh to the hot reservoir at Kelvin temperature Th, while work W is done on the engine’s working substance.
A household refrigerator removes heat from...
2.4K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

168
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
168
Regression Analysis01:11

Regression Analysis

6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

138
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
138
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.6K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Explainable machine learning for predicting hospital employees' quality of life using psychosocial work environment data.

Frontiers in public health·2025
Same author

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.

Sensors (Basel, Switzerland)·2022
Same author

Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET.

Sensors (Basel, Switzerland)·2022
Same author

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities.

Sensors (Basel, Switzerland)·2022
Same author

Computational Intelligence-Based Model for Exploring Individual Perception on SARS-CoV-2 Vaccine in Saudi Arabia.

Computational intelligence and neuroscience·2022
Same author

Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Aug 17, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

284

A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme

Irfan Ullah Khan1, Nida Aslam2, Rana AlShedayed2

  • 1SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary

This study introduces a machine learning (ML) and explainable artificial intelligence (XAI) model to proactively detect cybersecurity attacks on Internet of Things (IoT) devices in Industry 4.0 environments. The proposed model achieved high accuracy in identifying threats using HVAC log data.

Keywords:
Extreme Gradient BoostingInternet of Things (IoT)attackscyber securityexplainable artificial intelligence (XAI)information securitymachine learning

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

495
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K

Related Experiment Videos

Last Updated: Aug 17, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

284
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

495
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Industrial IoT

Background:

  • Industry 4.0 and the Internet of Things (IoT) offer advancements but introduce significant cybersecurity vulnerabilities.
  • Existing security systems struggle to proactively detect and analyze sophisticated cyber threats in real-time.
  • Heating, Ventilation, and Air Conditioning (HVAC) systems in industrial settings are critical infrastructure vulnerable to cyberattacks.

Purpose of the Study:

  • To develop a proactive and interpretable prediction model for detecting cybersecurity attacks on IoT devices.
  • To leverage Machine Learning (ML) and Explainable Artificial Intelligence (XAI) for enhanced threat detection.
  • To analyze HVAC system log data for identifying diverse security attack patterns.

Main Methods:

  • Implemented and compared several ML algorithms including Decision Tree, Random Forest, Gradient Boosting, Ada Boost, Light Gradient Boosting, XGBoost, and CatBoost.
  • Utilized stepwise forward feature selection (FFS) for optimal feature identification.
  • Addressed data imbalance using SMOTE (Synthetic Minority Over-sampling Technique) and Tomeklink, with SMOTE showing superior performance.
  • Applied XAI techniques (LIME and SHAP) for model interpretability.

Main Results:

  • The XGBoost classifier demonstrated exceptional performance, achieving an Area Under the Curve (AUC) of 0.9999, accuracy (ACC) of 0.9998, recall of 0.9996, precision of 1.000, and F1 Score of 0.9998.
  • SMOTE combined with selected features yielded the best results in mitigating data imbalance.
  • XAI methods provided local and global explanations, enhancing the transparency of the predictive model.

Conclusions:

  • Machine learning models are highly effective for predicting cybersecurity attacks on IoT devices within Industry 4.0.
  • The proposed interpretable ML-XAI model offers a robust solution for proactive threat detection in critical industrial systems.
  • Explainable AI is crucial for understanding and trusting AI-driven security predictions in complex environments.