Secure and Resilient Artificial Intelligence of Things: a HoneyNet Approach for Threat Detection and Situational Awareness

Liang Tan, Keping Yu, Fangpeng Ming, Xiaofeng Chen, Gautam Srivastava

Research output: Contribution to specialist publicationArticle

Abstract

Artificial Intelligence of Things (AIoT) is emerging as the future of Industry 4.0 and will be widely applied in consumer, commercial, and industrial fields. In AIoT, intelligent objects (smart devices), smart gateways, and edge/cloud nodes are subject to a large number of security threats and attacks. However, the traditional network security approaches are not fully suitable for AIoT. To address this issue, this paper proposes a HoneyNet approach that includes both threat detection and situational awareness to enhance the security and resilience of AIoT. We first design a HoneyNet based on Docker technology that collects data to detect adversaries and monitor their attack behaviors. The collected data are then converted into images and used as samples to train a deep learning model. Finally, the trained model is deployed in AIoT to perform threat detection and provide situational awareness. To validate our scheme, we conduct HoneyNet deployment and model training on the SiteWhere AIoT platform and construct a simulation environment on this platform for threat detection and situational awareness. The experimental results demonstrate the feasibility and effectiveness of our solution.

Original languageEnglish
Specialist publicationIEEE Consumer Electronics Magazine
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Consumer electronics
  • Deep learning
  • Intrusion detection
  • Monitoring
  • Security
  • Servers
  • Training

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications
  • Electrical and Electronic Engineering

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