American Journal of Neural Networks and Applications

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AI-Driven Security: How Machine Learning Will Shape the Future of Cybersecurity and Web 3.0

Received: 7 May 2023    Accepted: 26 May 2023    Published: 10 June 2023
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Abstract

As the world becomes increasingly digital, the need for advanced cybersecurity measures has never been greater. Cybersecurity is the practice of protecting computer systems, networks, and digital information from unauthorized access, theft, or damage. With the increasing reliance on digital technology in almost every aspect of modern life, the importance of cybersecurity has become paramount. The use of internet-connected devices has skyrocketed in recent years, with the number of devices expected to reach 20.4 billion by 2023, according to a report by Gartner. Traditional security methods are no longer sufficient to protect against sophisticated and evolving threats of today. Artificial intelligence (AI) offers a promising solution, with the potential to revolutionize the way we approach cybersecurity. In this paper, we explore the role of machine learning algorithms in security and their ability to automate tasks and reduce false positives. We also discuss the challenges and limitations of AI in security, including the lack of transparency in algorithms and the potential for vulnerability to hacking or manipulation. Looking towards the future, we predict that AI will play an even greater role in security and have a significant impact on Web 3.0 and other areas such as fraud detection and risk management.

DOI 10.11648/j.ajnna.20230901.11
Published in American Journal of Neural Networks and Applications (Volume 9, Issue 1, June 2023)
Page(s) 1-7
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Learning, Artificial Intelligence, Web 3.0

References
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  • APA Style

    Jasmin Praful Bharadiya. (2023). AI-Driven Security: How Machine Learning Will Shape the Future of Cybersecurity and Web 3.0. American Journal of Neural Networks and Applications, 9(1), 1-7. https://doi.org/10.11648/j.ajnna.20230901.11

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    ACS Style

    Jasmin Praful Bharadiya. AI-Driven Security: How Machine Learning Will Shape the Future of Cybersecurity and Web 3.0. Am. J. Neural Netw. Appl. 2023, 9(1), 1-7. doi: 10.11648/j.ajnna.20230901.11

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    AMA Style

    Jasmin Praful Bharadiya. AI-Driven Security: How Machine Learning Will Shape the Future of Cybersecurity and Web 3.0. Am J Neural Netw Appl. 2023;9(1):1-7. doi: 10.11648/j.ajnna.20230901.11

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  • @article{10.11648/j.ajnna.20230901.11,
      author = {Jasmin Praful Bharadiya},
      title = {AI-Driven Security: How Machine Learning Will Shape the Future of Cybersecurity and Web 3.0},
      journal = {American Journal of Neural Networks and Applications},
      volume = {9},
      number = {1},
      pages = {1-7},
      doi = {10.11648/j.ajnna.20230901.11},
      url = {https://doi.org/10.11648/j.ajnna.20230901.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20230901.11},
      abstract = {As the world becomes increasingly digital, the need for advanced cybersecurity measures has never been greater. Cybersecurity is the practice of protecting computer systems, networks, and digital information from unauthorized access, theft, or damage. With the increasing reliance on digital technology in almost every aspect of modern life, the importance of cybersecurity has become paramount. The use of internet-connected devices has skyrocketed in recent years, with the number of devices expected to reach 20.4 billion by 2023, according to a report by Gartner. Traditional security methods are no longer sufficient to protect against sophisticated and evolving threats of today. Artificial intelligence (AI) offers a promising solution, with the potential to revolutionize the way we approach cybersecurity. In this paper, we explore the role of machine learning algorithms in security and their ability to automate tasks and reduce false positives. We also discuss the challenges and limitations of AI in security, including the lack of transparency in algorithms and the potential for vulnerability to hacking or manipulation. Looking towards the future, we predict that AI will play an even greater role in security and have a significant impact on Web 3.0 and other areas such as fraud detection and risk management.},
     year = {2023}
    }
    

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    AB  - As the world becomes increasingly digital, the need for advanced cybersecurity measures has never been greater. Cybersecurity is the practice of protecting computer systems, networks, and digital information from unauthorized access, theft, or damage. With the increasing reliance on digital technology in almost every aspect of modern life, the importance of cybersecurity has become paramount. The use of internet-connected devices has skyrocketed in recent years, with the number of devices expected to reach 20.4 billion by 2023, according to a report by Gartner. Traditional security methods are no longer sufficient to protect against sophisticated and evolving threats of today. Artificial intelligence (AI) offers a promising solution, with the potential to revolutionize the way we approach cybersecurity. In this paper, we explore the role of machine learning algorithms in security and their ability to automate tasks and reduce false positives. We also discuss the challenges and limitations of AI in security, including the lack of transparency in algorithms and the potential for vulnerability to hacking or manipulation. Looking towards the future, we predict that AI will play an even greater role in security and have a significant impact on Web 3.0 and other areas such as fraud detection and risk management.
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Author Information
  • Department of Information and Technology, University of the Cumberlands, Fresno, USA

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