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Multi-Aspect Based Approach to Attack Detection in IoT Clouds.

Vasily Desnitsky1, Andrey Chechulin1, Igor Kotenko1

  • 1St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia.

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Summary
This summary is machine-generated.

This study introduces a machine learning approach for detecting network attacks in IoT cloud environments. The method enhances security by analyzing traffic patterns for improved threat identification.

Keywords:
IoTattack detectioncloudnetwork security

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Internet of Things (IoT) cloud infrastructure faces unique network attack vectors.
  • Limited data flows from IoT devices pose challenges for traditional security monitoring.
  • Effective detection of sophisticated network attacks is crucial for IoT security.

Purpose of the Study:

  • To develop and evaluate tools for detecting network attacks targeting IoT cloud devices.
  • To propose a robust, multi-aspect approach for enhanced attack detection accuracy.
  • To validate the effectiveness of machine learning models in identifying diverse attack types.

Main Methods:

  • Utilized machine learning algorithms including AdaBoostClassifier, RandomForestClassifier, and MultinomialNB.
  • Developed a combined approach using session-based, host-based, and feature-extracted traffic spaces.
  • Implemented an attack-specific ensemble of machine learning methods for improved detection.
  • Trained and tested models using existing benign and attacking traffic samples.

Main Results:

  • Constructed and validated machine learning models for IoT network attack detection.
  • Achieved high precision, recall, and F1-measure scores for various attack types.
  • Demonstrated the effectiveness of the multi-aspect and ensemble learning approach.
  • Confirmed the correctness and practical applicability of the developed detection tools.

Conclusions:

  • The proposed machine learning framework effectively detects network attacks in IoT cloud settings.
  • The multi-aspect feature extraction and ensemble methods significantly improve detection performance.
  • The developed tools offer a reliable solution for enhancing IoT cloud security.