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Effects of EDTA on End-Point Detection Methods01:18

Effects of EDTA on End-Point Detection Methods

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Related Experiment Video

Updated: Jun 26, 2026

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Enhancing DDoS detection in SDIoT through effective feature selection with SMOTE-ENN.

Arati Behera1, Kshira Sagar Sahoo1,2, Tapas Kumara Mishra1

  • 1Department of Computer Science and Engineering, SRM University, Amaravati, Andhra Pradesh, India.

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|October 17, 2024
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Summary

This study introduces a novel method for detecting Distributed Denial of Services (DDoS) attacks in the Internet of Things (IoT). The improved feature selection technique achieves high accuracy in identifying threats within the Software Defined-Internet of Things (SDIoT) ecosystem.

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Area of Science:

  • Cybersecurity
  • Network Security
  • Internet of Things (IoT)

Background:

  • The Internet of Things (IoT) enables device connectivity but introduces Distributed Denial of Services (DDoS) threats.
  • Software Defined-Internet of Things (SDIoT) offers enhanced security but faces challenges like limited resources and protocol heterogeneity.
  • There is a critical need for efficient and low-cost DDoS attack classifiers in SDIoT environments.

Purpose of the Study:

  • To develop an improved feature selection (FS) technique for enhanced DDoS attack detection in IoT.
  • To reduce training time and improve the detection rate of DDoS attacks in Software Defined-Internet of Things (SDIoT).
  • To address data imbalance and multicollinearity issues in DDoS detection datasets.

Main Methods:

  • Data imbalance was addressed using Edited Nearest Neighbor-based Synthetic Minority Oversampling (SMOTE-ENN).
  • A novel FS method, SFMI, combining Sequential Feature Selection (SFE) and Mutual Information (MI), was proposed.
  • Principal Component Analysis (PCA) was used to handle multicollinearity, followed by classification using Random Forest (RF).

Main Results:

  • The proposed SMOTE-ENN+SFMI+PCA approach with RF classifier achieved 99.97% accuracy.
  • The method demonstrated a precision of 99.39% using only 10 selected features.
  • Experiments were validated on KDDCup99 and CIC IoT-2023 benchmark datasets.

Conclusions:

  • The integrated approach effectively enhances DDoS detection accuracy and efficiency in IoT networks.
  • The proposed feature selection method significantly reduces the number of features required for high-performance classification.
  • This research offers a cost-effective solution for securing the growing SDIoT ecosystem against sophisticated cyber threats.