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Related Experiment Video

Updated: Jul 11, 2026

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

EHFOA-ID: An Enhanced HawkFish Optimization-Driven Hybrid Ensemble for IoT Intrusion Detection.

Ashraf Nadir Alswaid1, Osman Nuri Uçan1

  • 1Altinbas University, Istanbul 34217, Turkey.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces EHFOA-ID, an advanced intrusion detection system for Internet of Things (IoT) environments. It significantly improves accuracy and reduces false alarms by optimizing feature selection and using a hybrid deep ensemble for robust threat identification.

Keywords:
HawkFish Optimizationdeep ensemble learningfeature selectionintrusion detection system

Related Experiment Videos

Last Updated: Jul 11, 2026

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Internet of Things (IoT)

Background:

  • IoT environments face complex intrusion detection challenges due to high-dimensional data and diverse attack vectors.
  • Existing methods struggle with class imbalance and efficient feature space navigation.

Purpose of the Study:

  • To propose EHFOA-ID, an enhanced intrusion detection framework for IoT.
  • To address challenges of high-dimensional traffic, heterogeneous attacks, and class imbalance.

Main Methods:

  • Utilizes an Enhanced HawkFish Optimization Algorithm (EHFOA) for joint feature selection and hyperparameter tuning.
  • Employs a hybrid deep ensemble learning model to capture spatial, temporal, and contextual relationships.
  • Integrates adaptive exploration-exploitation, Lévy flight, and diversity preservation for optimization.

Main Results:

  • Achieved >99% accuracy on UNSW-NB15 and >98% on SECOM datasets.
  • Reported macro-F1 scores above 0.97 and false alarm rates below 2%.
  • Demonstrated superior performance over state-of-the-art intrusion detection approaches.

Conclusions:

  • EHFOA-ID offers a robust and efficient solution for IoT intrusion detection.
  • The unified optimization-learning pipeline enhances generalization and reduces feature redundancy.
  • The framework effectively improves decision reliability against diverse intrusion patterns.