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Examining the Suitability of NetFlow Features in Detecting IoT Network Intrusions.

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  • 1Department of Computer Science and Engineering, American University of Ras Al Khaimah, Ras Al Khaimah P.O. Box 72603, United Arab Emirates.

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Summary

This study introduces an efficient Intrusion Detection System (IDS) using Machine Learning (ML) and NetFlow data. It achieves high accuracy in detecting cyberattacks with minimal features, enhancing network security.

Keywords:
Internet of ThingsNetwork Intrusion Detection Systemcyber securityfeature selectionmachine learning

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Area of Science:

  • Cybersecurity
  • Machine Learning Applications
  • Network Traffic Analysis

Background:

  • Increasing cyberattacks on Internet of Things (IoT) devices threaten organizational security and user privacy.
  • Machine Learning (ML) in Intrusion Detection Systems (IDS) is crucial for detecting novel zero-day attacks.
  • The prediction time and feature selection significantly impact the performance of anomaly-based Network Intrusion Detection Systems (NIDS).

Purpose of the Study:

  • To examine NetFlow features for their suitability in classifying network traffic.
  • To develop an efficient ML model for detecting cyberattacks in IoT networks.
  • To reduce the number of features required for accurate network intrusion detection.

Main Methods:

  • Analysis of NetFlow protocol features for network traffic classification.
  • Development and evaluation of a Machine Learning model using a large dataset (over 16 million records from 2021).
  • Feature selection to identify the most impactful features for attack detection.

Main Results:

  • A model was developed that accurately detects network attacks with 98-100% accuracy.
  • The model effectively utilizes a minimal set of 13 NetFlow features.
  • The study confirms the suitability of selected NetFlow features for robust network traffic classification.

Conclusions:

  • Optimized feature selection in ML-based NIDS can significantly improve detection accuracy and processing speed.
  • NetFlow data provides valuable features for building highly effective and efficient cybersecurity solutions.
  • The proposed model offers a promising approach to enhance the security of IoT devices and networks against sophisticated cyber threats.