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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Securing IoT networks: a machine learning approach for detecting unusual traffic patterns.

Nadeem Sarwar1, Raed S Alharthi2, Mansourah Aljohani3

  • 1Department of Computer Science, Bahria University Lahore Campus, Lahore, 54600, Pakistan. nadeem_srwr@yahoo.com.

Scientific Reports
|December 27, 2025
PubMed
Summary

This study presents a machine learning (ML) framework for Internet of Things (IoT) security, effectively detecting anomalous traffic. A neural network model achieved superior performance in identifying and preventing IoT network anomalies.

Keywords:
Anomaly detectionData preprocessingDecision treesFeature engineeringIoT securityMachine learningNetwork security

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07:15

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Published on: August 16, 2020

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • The rapid expansion of the Internet of Things (IoT) presents significant security vulnerabilities due to its complex and distributed architecture.
  • Existing security measures struggle to cope with the scale and diversity of IoT networks, necessitating advanced solutions.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) framework for enhancing the security of Internet of Things (IoT) networks.
  • To identify and mitigate anomalous traffic patterns indicative of security threats within IoT environments.

Main Methods:

  • Utilized a comprehensive data preprocessing pipeline, including cleaning, integration, transformation, and feature engineering.
  • Integrated the NBaIoT and UNSW-NB15 datasets to create a robust environment for analysis.
  • Assessed the performance of various ML models, including Decision Tree, SVM, Random Forest, and a Neural Network.

Main Results:

  • The Decision Tree model achieved 95% accuracy, SVM 96%, and Random Forest 97%.
  • The neural network model demonstrated superior performance with 98% precision and 97% recall.
  • The neural network-based framework proved more effective in detecting and preventing IoT traffic anomalies compared to other evaluated models.

Conclusions:

  • Machine learning offers a promising approach for developing robust, scalable, and real-time anomaly detection solutions for IoT networks.
  • The proposed neural network framework shows significant potential for improving IoT security.
  • Future research should focus on practical implementation and incorporating additional datasets and ML methods for enhanced model flexibility and resilience.