Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods

Tehseen Mazhar (), Hafiz Muhammad Irfan, Sunawar Khan, Inayatul Haq, Inam Ullah, Muhammad Iqbal and Habib Hamam ()
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Tehseen Mazhar: Department of Computer Science, Virtual University of Pakistan, Lahore 51000, Pakistan
Hafiz Muhammad Irfan: Department of Computer Science, Islamia University Bahawalpur, Bahawalnagar 62300, Pakistan
Sunawar Khan: Department of Computer Science, Islamia University Bahawalpur, Bahawalnagar 62300, Pakistan
Inayatul Haq: School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Inam Ullah: BK21 Chungbuk Information Technology Education and Research Center, Chungbuk National University, Cheongju 28644, Republic of Korea
Muhammad Iqbal: Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan 29220, Pakistan
Habib Hamam: Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada

Future Internet, 2023, vol. 15, issue 2, 1-37

Abstract: Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur.

Keywords: smart grid; cyber security; cyberattacks; machine learning; deep learning; data mining (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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