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Related Experiment Video

Updated: Jan 7, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Intrusion detection using search-based learning optimized ensemble tree classifier model.

Afnan M Alhassan1, Nouf I Altmami1

  • 1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia.

Plos One
|December 29, 2025
PubMed
Summary
This summary is machine-generated.

A new Search-based learning-optimized ensemble tree classifier enhances cybersecurity by improving intrusion detection in Vehicular Ad Hoc Networks. This adaptive system effectively identifies and categorizes various cyber threats with high accuracy.

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Area of Science:

  • Cybersecurity and Network Engineering
  • Machine Learning for Security
  • Intrusion Detection Systems (IDS)

Background:

  • Traditional Intrusion Detection Systems (IDS) struggle with false positives/negatives, evolving threats, high-dimensional data, and privacy concerns.
  • Existing host-based IDS (HIDS) and network-based IDS (NIDS) have limitations in adapting to complex and dynamic network environments like VANETs.

Purpose of the Study:

  • To develop an adaptive and effective intrusion detection system to address limitations of existing models.
  • To improve the security of digital environments, specifically within Vehicular Ad Hoc Networks (VANETs).

Main Methods:

  • Application of a Search-based learning-optimized ensemble tree classifier (SBO-based ensemble tree classifier) for intrusion detection in VANETs.
  • Fusion of multiple classifiers including Decision Tree, Random Forest, Extra Tree, and XGBoost within the ensemble.
  • Incorporation of Search-based learning optimization to enhance the collective and adaptive nature of the ensemble model.

Main Results:

  • The proposed SBO-based ensemble tree classifier achieved high performance metrics on the BOT-IOT Dataset.
  • Key performance indicators include 96.56% accuracy, 96.63% F1-score, 0.97 MCC, and 96.68% sensitivity.
  • The model demonstrated superior performance compared to existing methods for intrusion detection.

Conclusions:

  • The SBO-based ensemble tree classifier offers a robust and effective solution for intrusion detection in VANETs.
  • The multi-dimensional output (alpha, beta, gamma, delta) facilitates specific categorization of intrusion attacks.
  • The research highlights the potential of optimized ensemble methods in advancing cybersecurity defenses.