IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i2id1145.html

IoT-Enabled Cybersecurity for Datacenters - Real-Time Threat Monitoring and Incident Response

Author

Listed:
  • Mahesh Kolli

Abstract

This article presents a comprehensive analysis of an IoT-enabled cybersecurity implementation at a major data center service provider operating across multiple regions. It examines the integration of advanced artificial intelligence and machine learning models with IoT sensors to enhance threat detection and incident response capabilities. The platform's innovative approach combines real-time infrastructure monitoring with automated response mechanisms, leveraging continuous learning algorithms to adapt to evolving cyber threats. By analyzing patterns in power consumption, cooling systems, and network behavior, the system demonstrates significant improvements in threat detection and response efficiency. The implementation showcases how intelligent IoT integration in data center security can strengthen operational resilience while ensuring regulatory compliance across critical sectors including banking, healthcare, and telecommunications.

Suggested Citation

  • Mahesh Kolli, 2025. "IoT-Enabled Cybersecurity for Datacenters - Real-Time Threat Monitoring and Incident Response," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(2), pages 739-746, March.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1145
    DOI: 10.32628/CSEIT25112412
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112412
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT25112412
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/home/article/download/CSEIT25112412/CSEIT25112412
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.32628/CSEIT25112412?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1145. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.