Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

Sandesh Achar, Nuruzzaman Faruqui, Md Whaiduzzaman, Albara Awajan, Moutaz Alazab

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen cyber-physical security (CPS) with minimal human intervention. It is an approach based on human activity recognition (HAR), where GoogleNet–BiLSTM network hybridization has been used to recognize suspicious activities in the cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates machine vision at the IoT edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and the GoogleNet–BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security at only USD (Formula presented.) per month.

Original languageEnglish
Article number1892
JournalElectronics (Switzerland)
Volume12
Issue number8
DOIs
Publication statusPublished - Apr 2023

Keywords

  • algorithm
  • BiLSTM
  • cyber-physical security
  • deep learning
  • GoogleNet
  • human activity recognition

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