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

Classification of Systems-I01:26

Classification of Systems-I

236
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
236
Classification of Systems-II01:31

Classification of Systems-II

192
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
192
PD Controller: Design01:26

PD Controller: Design

300
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
300

You might also read

Related Articles

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

Sort by
Same author

Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.

PeerJ. Computer science·2025
Same author

Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification.

Sensors (Basel, Switzerland)·2022
Same author

Prospect of Internet of Medical Things: A Review on Security Requirements and Solutions.

Sensors (Basel, Switzerland)·2022
Same author

Comprehensive Performance Analysis of Zigbee Communication: An Experimental Approach with XBee S2C Module.

Sensors (Basel, Switzerland)·2022
Same author

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework.

PeerJ. Computer science·2021
Same author

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN.

PeerJ. Computer science·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 2, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning: A Comparative Performance

Bifta Sama Bari1, Kumar Yelamarthi1, Sheikh Ghafoor2

  • 1Department of Electrical and Computer Engineering, Tennessee Tech University, Cookeville, TN 38501, USA.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning intrusion detection system (IDS) for vehicles, achieving 99.9% accuracy in detecting cyberattacks on the Controller Area Network (CAN) protocol. The system effectively mitigates automotive cybersecurity threats.

Keywords:
CANcyber-physical systemintrusion detectionmachine learningvehicle security

More Related Videos

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Related Experiment Videos

Last Updated: Aug 2, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Area of Science:

  • Automotive Cybersecurity
  • Machine Learning Applications
  • Network Intrusion Detection

Background:

  • Modern vehicles rely heavily on Electronic Control Units (ECUs) for critical operations, comfort, and safety.
  • The Controller Area Network (CAN) protocol used for ECU communication presents significant security vulnerabilities, leading to numerous automotive cyber incidents.
  • Existing security measures are insufficient, necessitating advanced intrusion detection systems.

Purpose of the Study:

  • To develop and evaluate a machine learning-based Intrusion Detection System (IDS) for automotive networks.
  • To investigate the effectiveness of Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN) algorithms for detecting CAN protocol intrusions.
  • To validate the proposed IDS on real-world vehicular datasets.

Main Methods:

  • Implementation of an IDS utilizing machine learning algorithms: SVM, DT, and KNN.
  • Training and testing the IDS on diverse vehicular datasets, including Kia Soul and Chevrolet Spark.
  • Performance evaluation based on accuracy, true positive rate, and false negative rate.

Main Results:

  • The proposed IDS achieved a high detection accuracy of up to 99.9%.
  • The system demonstrated a high true positive rate and a low false negative rate, indicating reliable intrusion detection.
  • Comparative analysis showed the IDS outperforms existing methods in terms of reliability and efficiency.

Conclusions:

  • The developed machine learning-based IDS is highly effective and reliable for detecting cyberattacks in vehicular networks.
  • The proposed system offers a robust solution for mitigating security vulnerabilities within the Controller Area Network (CAN) protocol.
  • This research contributes to enhancing automotive cybersecurity through advanced intrusion detection techniques.