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Related Experiment Videos

Study of Machine Learning for Cloud Assisted IoT Security as a Service.

Maram Alsharif1, Danda B Rawat1

  • 1Data Science and Cybersecurity Center, Howard University, Washington, DC 20059, USA.

Sensors (Basel, Switzerland)
|February 6, 2021
PubMed
Summary

Machine learning intrusion detection systems (ML-IDS) for IoT devices face resource limitations. This paper proposes a cloud-based service to manage ML models, offloading intensive tasks for efficient IoT security.

Keywords:
cloud assisted IoT security as a servicemachine learning

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Area of Science:

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

Background:

  • Machine learning (ML) is increasingly used in intrusion detection systems (IDS) for securing IoT devices.
  • Current ML-IDS research prioritizes prediction effectiveness over operational resource demands, especially for resource-constrained embedded systems.
  • ML techniques require significant computational resources, posing challenges for direct implementation on many IoT devices.

Purpose of the Study:

  • To propose a cloud-based service architecture for managing ML models tailored to diverse IoT device configurations.
  • To address the operational requirements of ML-IDS by offloading resource-intensive tasks from IoT devices to the cloud.
  • To reduce the maintenance workload on IoT devices while enhancing their security posture through cloud-managed ML models.

Main Methods:

  • Development of a cloud-based service architecture for ML model management.
  • Offloading of computationally demanding ML activities including feature selection, model building, training, and validation to the cloud.
  • Provision of optimized security models back to IoT devices as a service.

Main Results:

  • The proposed architecture enables IoT devices to leverage powerful ML models without requiring substantial local computing resources.
  • Offloading heavy-weight ML tasks significantly reduces the processing and maintenance burden on individual IoT devices.
  • This approach facilitates the deployment of effective ML-IDS across a wide range of IoT operational configurations.

Conclusions:

  • A cloud-based service architecture offers a practical solution for deploying resource-efficient ML-IDS on IoT devices.
  • This model enhances IoT security by making advanced ML capabilities accessible, overcoming hardware limitations.
  • The proposed system effectively balances security needs with the operational constraints of embedded IoT systems.