Vulnerability assessment of 6g-enabled smart grid cyber-physical systems

Muhammad Tariq, Mansoor Ali, Faisal Naeem, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


Next-generation wireless communication and networking technologies, such as sixth-generation (6G) networks and software-defined Internet of Things (SDIoT), make cyber-physical systems (CPSs) more vulnerable to cyberattacks. In such massively connected CPSs, an intruder can trigger a cyberattack in the form of false data injection, which can lead to system instability. To address this issue, we propose a graphics-processing-unit-enabled adaptive robust state estimator. It comprises a deep learning algorithm, long short-term memory, and a nonlinear extended Kalman filter, and is called LSTMKF. Through an SDIoT controller, it provides an online parametric state estimate. The reliability is improved by performing two levels of online parametric state estimation for secure communication and load management. The CPS under study is a 6G and SDIoT-enabled smart grid, which is tested on IEEE 14, 30, and 118 bus systems. Compared to existing techniques, the proposed algorithm is able to estimate the state variables of the system even during or after a cyberattack, with lower time complexity and high accuracy.

Original languageEnglish (US)
Article number9276446
Pages (from-to)5468-5475
Number of pages8
JournalIEEE Internet of Things Journal
Issue number7
StatePublished - Apr 1 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Cyber security
  • cyber-physical system (CPS)
  • sixth generation (6G)
  • smart grids
  • software-defined Internet of Things (SDIoT)
  • vulnerability


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