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Aggregates Classification01:29

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet

Mohammed Al-Sarem1, Faisal Saeed1,2, Eman H Alkhammash3

  • 1College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia.

Sensors (Basel, Switzerland)
|January 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient method for detecting Internet of Things (IoT) botnet attacks using aggregated mutual information for feature selection. The approach enhances machine learning model performance for improved IoT security.

Keywords:
Internet of Thingsbotnet attack detectionensemble methodsfeature selectionintrusion detection systemsmachine learning

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Internet of Things (IoT)

Background:

  • Internet of Things (IoT) networks face increasing botnet attacks due to widespread device usage.
  • Existing Intrusion Detection Systems (IDS) for IoT struggle with efficiency due to device limitations (battery, processor).
  • There is a need for efficient and effective IoT botnet detection methods with low computational time and high accuracy.

Purpose of the Study:

  • To propose an aggregated mutual information-based feature selection approach for enhancing the detection of IoT botnet attacks.
  • To improve the efficiency and effectiveness of Intrusion Detection Systems (IDS) in resource-constrained IoT environments.
  • To evaluate the performance of machine learning classifiers using the proposed feature selection method on real IoT traffic data.

Main Methods:

  • Utilized the N-BaIoT benchmark dataset comprising real traffic data from nine commercial IoT devices.
  • Employed an aggregated mutual information-based feature selection technique, incorporating Mutual Information (MI), Principal Component Analysis (PCA), and ANOVA f-test.
  • Evaluated several ensemble and individual machine learning classifiers, including Random Forest (RF), XGBoost (XGB), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (k-NN), Logistic Regression (LR), and Support Vector Machine (SVM).

Main Results:

  • The proposed aggregated mutual information-based feature selection approach significantly enhanced the detection performance of IoT botnet classifiers.
  • The method demonstrated efficiency and effectiveness, outperforming other techniques across various evaluation metrics.
  • The feature selection process was applied at a finely-granulated detection level to identify the most relevant features for classification.

Conclusions:

  • The developed feature selection approach is effective in improving the accuracy and efficiency of IoT botnet attack detection.
  • This method offers a promising solution for building robust and resource-aware Intrusion Detection Systems (IDS) for the Internet of Things (IoT).
  • The study highlights the importance of optimized feature selection for machine learning-based cybersecurity in IoT ecosystems.