Security risk models against attacks in smart grid using big data and artificial intelligence

• Published in: PeerJ. Computer science 2024-04, Vol.10, p.e1840-e1840, Article e1840
• Main Authors: Yasin Ghadi, Yazeed, Mazhar, Tehseen, Aurangzeb, Khursheed, Haq, Inayatul, Shahzad, Tariq, Ali Laghari, Asif, Shahid Anwar, Muhammad
• Format: Article
• Language: English
• Subjects: Algorithms and Analysis of Algorithms; Artificial Intelligence; Big data; Blockchain; Cybersecurity; Cyberterrorism; Data Mining and Machine Learning; Deep learning; Infrastructure (Economics); Machine learning; Risk assessment; Security and Privacy; Security management; Smart grid
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1840

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.