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An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks.

Andrew Churcher1, Rehmat Ullah2, Jawad Ahmad1

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Summary
This summary is machine-generated.

Machine learning (ML) enhances intrusion detection systems (IDS) for Internet of Things (IoT) security. Random Forest excels in binary classification, while K-Nearest Neighbors leads in multi-class detection for IoT networks.

Keywords:
Internet of Things (IoT)IoT attacksML modelsintrusion detection systemsmachine learningmulti-class classificationprivacysecurity

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Area of Science:

  • Cybersecurity
  • Machine Learning Applications
  • Network Security

Background:

  • The proliferation of Internet of Things (IoT) devices generates vast data, posing security challenges due to their resource constraints.
  • Traditional intrusion detection systems (IDS) struggle to efficiently manage the increasing complexity and volume of cyberattacks targeting IoT networks.
  • Vulnerabilities in IoT devices are often overlooked, creating significant incentives for attackers.

Purpose of the Study:

  • To evaluate and compare the effectiveness of various machine learning (ML) algorithms for intrusion detection in IoT environments.
  • To assess the performance of ML models in both binary and multi-class classification scenarios using the Bot-IoT dataset.
  • To provide empirical evidence on the suitability of different ML techniques for enhancing IoT network security.

Main Methods:

  • Utilized several machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), Artificial Neural Network (ANN), and Logistic Regression (LR).
  • Performed comparative analysis on the Bot-IoT dataset, evaluating models for binary and multi-class classification tasks.
  • Assessed algorithm performance using key metrics: accuracy, precision, recall, F1 score, and log loss.

Main Results:

  • Random Forest (RF) achieved 99% accuracy for detecting HTTP distributed denial-of-service (DDoS) attacks in binary classification.
  • RF demonstrated superior performance across all attack types in binary classification, based on precision, recall, F1 score, and log loss.
  • K-Nearest Neighbors (KNN) achieved the highest accuracy (99%) in multi-class classification, outperforming RF by 4%.

Conclusions:

  • Machine learning algorithms offer a viable solution for improving intrusion detection in resource-constrained IoT environments.
  • Random Forest is highly effective for binary classification tasks in IoT security, particularly against DDoS attacks.
  • K-Nearest Neighbors shows significant promise for multi-class intrusion detection in IoT networks, offering higher accuracy than other evaluated methods.