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


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 cyber attacks. In such massively connected CPSs, an intruder can trigger a cyber attack in the form of false data injection, which can lead to system instability. To solve this issue of massive connections and states generated in a CPS, we propose a graphical-processing-unit enabled adaptive robust state estimator. It comprises of deep learning algorithm, long short-term memory, and a non-linear 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 cyber attack, with lower time complexity and high accuracy.

Original languageEnglish (US)
JournalIEEE Internet of Things Journal
StateAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

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


  • 6G
  • 6G mobile communication
  • cyber physical system
  • cyber security
  • Internet of Things
  • Phasor measurement units
  • Power system reliability
  • Reliability
  • Smart grids
  • smart grids.
  • software defined Internet of things
  • State estimation
  • Vulnerability

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