Jove
Visualize
Contact Us

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.1K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.1K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

150
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...
150

You might also read

Related Articles

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

Sort by
Same authorSame journal

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

Sensors (Basel, Switzerland)·2026
Same author

Adaptive Image Deblurring Convolutional Neural Network with Meta-Tuning.

Sensors (Basel, Switzerland)·2025
Same author

EHNet: Efficient Hybrid Network with Dual Attention for Image Deblurring.

Sensors (Basel, Switzerland)·2024
Same author

Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms.

Sensors (Basel, Switzerland)·2024
Same author

A Novel Architecture for an Intrusion Detection System Utilizing Cross-Check Filters for In-Vehicle Networks.

Sensors (Basel, Switzerland)·2024
Same author

CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-Class and Intra-Class Variations.

Sensors (Basel, Switzerland)·2023
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

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
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 Experiment Video

Updated: Sep 16, 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

Adaptive Autoencoder-Based Intrusion Detection System with Single Threshold for CAN Networks.

Donghyeon Kim1, Hyungchul Im1, Seongsoo Lee1

  • 1Department of Intelligent Semiconductors, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight, unsupervised intrusion detection system (IDS) for controller area network (CAN) bus security. The novel autoencoder model effectively detects various CAN attacks in real-time with high accuracy.

Keywords:
Gaussian kernel density estimationcontroller area networkcybersecuritydeep learningin-vehicle networkintrusion detection systemlightweight

More Related Videos

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

5.8K
Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.2K

Related Experiment Videos

Last Updated: Sep 16, 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
A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

5.8K
Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.2K

Area of Science:

  • Cybersecurity
  • Automotive Engineering
  • Network Security

Background:

  • Controller Area Network (CAN) protocol is crucial for in-vehicle communication but lacks inherent security, making it vulnerable to attacks.
  • Existing intrusion detection systems (IDS) for CAN networks are often complex and not optimized for real-time, on-device implementation.
  • Need for robust and efficient security solutions to protect modern vehicles from cyber threats.

Purpose of the Study:

  • To propose a novel, lightweight, unsupervised IDS for CAN networks suitable for real-time, on-device deployment.
  • To develop an autoencoder-based model trained on normal CAN data for effective attack detection.
  • To optimize the system for hardware implementation on Field-Programmable Gate Arrays (FPGAs).

Main Methods:

  • An autoencoder model was trained exclusively on normal CAN traffic data.
  • Gaussian kernel density estimation and error rate analysis were used to determine the optimal detection threshold and frame count.
  • The model was validated on an FPGA using unseen attack data, employing a single detection threshold for all attack types.

Main Results:

  • The proposed IDS achieved high performance metrics: 99.2% average accuracy, 99.2% precision, 99.1% recall, and 99.2% F1-score.
  • The system demonstrated effective detection of four different types of attacks not encountered during training.
  • Significant reduction in hardware resource utilization (LUTs, flip-flops) and power consumption compared to existing FPGA-based IDS.

Conclusions:

  • The developed lightweight unsupervised IDS offers a highly accurate and efficient solution for securing CAN networks in real-time.
  • The FPGA implementation provides a practical and resource-efficient approach for on-device intrusion detection in vehicles.
  • The system's ability to detect diverse attacks with a single model and threshold highlights its robustness and adaptability.