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Multi-Source Knowledge Reasoning for Data-Driven IoT Security.

Shuqin Zhang1, Guangyao Bai1, Hong Li2

  • 1School of Computer Science, Zhongyuan University of Technology, Zhengzhou 450007, China.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the IoT Security Threat Ontology (IoTSTO) to unify diverse cyber security knowledge bases for better IoT threat analysis. It enables automated threat assessment and mitigation inference for enhanced IoT security management.

Keywords:
IoT securityinference rulesknowledge reasoningontologythreat analysis

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Area of Science:

  • Cybersecurity
  • Ontology Engineering
  • Internet of Things (IoT) Security

Background:

  • Public knowledge bases for cyber security vulnerabilities and threat intelligence exist but suffer from heterogeneity.
  • Complexity of IoT environments complicates network security situation awareness and threat assessment.
  • Existing cyber security ontologies lack comprehensive coverage for IoT-specific threats.

Purpose of the Study:

  • To integrate diverse cyber security data sources (vulnerabilities, attack patterns, etc.) into a unified framework.
  • To propose the IoT Security Threat Ontology (IoTSTO) for describing IoT security threats.
  • To develop a multi-source knowledge reasoning method for enhanced IoT threat analysis.

Main Methods:

  • Integration of vulnerabilities, weaknesses, affected platforms, tactics, attack techniques, and patterns.
  • Development of the IoT Security Threat Ontology (IoTSTO).
  • Design of inference rules for multi-source knowledge reasoning within the IoTSTO model.

Main Results:

  • The IoTSTO model successfully integrates heterogeneous cyber security knowledge bases.
  • The multi-source knowledge reasoning method can assess IoT threats, infer mitigations, and identify vulnerable nodes.
  • Semantic heterogeneity of multi-source knowledge for IoT security is addressed.

Conclusions:

  • The proposed IoTSTO and reasoning method provide a unified, extensible, and reusable approach for IoT security analysis.
  • This work enhances IoT security situation awareness and decision-making for security managers.
  • The study bridges the gap in current cyber security ontology modeling for IoT environments.