TY - JOUR
T1 - Vulnerability assessment of 6g-enabled smart grid cyber-physical systems
AU - Tariq, Muhammad
AU - Ali, Mansoor
AU - Naeem, Faisal
AU - Poor, H. Vincent
N1 - Funding Information:
Manuscript received August 1, 2020; revised October 14, 2020 and November 7, 2020; accepted November 23, 2020. Date of publication December 2, 2020; date of current version March 24, 2021. This work was supported in part by the U.S. National Science Foundation under Grant CCF-1908308 and Grant ECCS-1824710. (Corresponding author: Muhammad Tariq.) Muhammad Tariq, Mansoor Ali, and Faisal Naeem are with the Electrical Engineering Department, National University of Computer and Emerging Sciences (Peshawar Campus), Peshawar 25000, Pakistan (e-mail: mtariq@princeton.edu).
Publisher Copyright:
© 2014 IEEE.
PY - 2021/4/1
Y1 - 2021/4/1
N2 - 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.
AB - 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.
KW - Cyber security
KW - cyber-physical system (CPS)
KW - sixth generation (6G)
KW - smart grids
KW - software-defined Internet of Things (SDIoT)
KW - vulnerability
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U2 - 10.1109/JIOT.2020.3042090
DO - 10.1109/JIOT.2020.3042090
M3 - Article
AN - SCOPUS:85097366285
SN - 2327-4662
VL - 8
SP - 5468
EP - 5475
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 7
M1 - 9276446
ER -