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 Experiment Videos

Toward Adversarial Robustness Network Intrusion Detection Based on Multi-Model Ensemble Approach.

Thi-Thu-Huong Le1, Jaehan Cho2, Dawit Shin2

  • 1Blockchain Platform Research Center, Pusan National University, Busan 46241, Republic of Korea.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

544
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 of...
544

You might also read

Related Articles

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

Sort by
Same author

A Robust Framework for Coffee Bean Package Label Recognition: Integrating Image Enhancement with Vision-Language OCR Models.

Sensors (Basel, Switzerland)·2025
Same author

Enhancing Security Operations Center: Wazuh Security Event Response with Retrieval-Augmented-Generation-Driven Copilot.

Sensors (Basel, Switzerland)·2025
Same author

CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher.

Sensors (Basel, Switzerland)·2024
Same author

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory.

Sensors (Basel, Switzerland)·2023
Same author

Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification.

Sensors (Basel, Switzerland)·2023
Same author

DEMIX: Domain-Enforced Memory Isolation for Embedded System.

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

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

Machine learning network intrusion detection systems (NIDS) face adversarial threats. This study shows defenses are dataset-specific, with median filtering being a fragile component, and no universal defense exists for tabular NIDS data.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • Machine learning-based network intrusion detection systems (NIDS) are susceptible to adversarial attacks.
  • Existing robustness research for tabular NIDS data is limited by single-model, single-dataset, and non-adaptive evaluations.

Purpose of the Study:

  • To conduct a comparative robustness study of a four-component defense pipeline for tabular NIDS.
  • To evaluate the dataset and architecture dependence of adversarial defenses.

Main Methods:

  • Evaluated XGBoost, LightGBM, TabNet, and Residual MLP on RT_IOT2022 and Web_IDS23 datasets.
  • Assessed performance under standard, constrained, and adaptive attacks, including component-wise ablations and sensitivity analyses.
  • Measured per-class F1 scores and computational overhead.
Keywords:
adversarial attacksadversarial robustnessattack success rate (ASR)defense mechanismsgradient-based attacksintrusion detection systems (IDSs)network security

Related Experiment Videos

Main Results:

  • Defense effectiveness shows strong dependence on dataset and model architecture.
  • Tree-based models on RT_IOT2022 reduced robustness gaps but impacted clean accuracy; Residual MLP offered a better balance.
  • On Web_IDS23, simpler defenses sometimes outperformed the full pipeline; median filtering proved to be the most fragile component.

Conclusions:

  • Adversarial defense for tabular NIDS is validation-driven and dataset-specific.
  • The evaluated four-component defense stack is not a universal solution and requires careful tuning.
  • Deployment limitations include minority-class collapse and significant training costs.