Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sharma, Pradeep; Logeshwaran, J. Title A name given to the resource Real-Time Monitoring and Anomaly Detection in Cloud-Based IoT Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Networks and Cryptology, NETCRYPT 2025;pp.448-452 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/NETCRYPT65877.2025.11102267" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NETCRYPT65877.2025.11102267</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015531970?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015531970?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sharma P., Service Now, United States; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, 560029, India Description An account of the resource IoT devices have seen explosive growth, causing a data explosion, which makes it almost impossible to manage and monitor these networks. This has led to a demand for solutions providing real-time monitoring and detection of anomalies in cloud-based Internet of Things (IoT) networks. In cybersecurity, the term real-time monitoring denotes the ongoing analysis of data and network performance to detect potential problems. It allows for the identification of anomalies and possible weaknesses in the system. In contrast, anomaly detection is the process of finding deviations from what is considered normal for a system or data. Within cloud-based IoT networks, this might involve identifying abnormal traffic patterns or unusual activity from devices. To solve these challenges, we propose a cloud computing and machine learning-based solution. IoT devices generate a massive amount of data, which is processed in real-time and stored on the cloudbased infrastructure. Many machine learning algorithms analyze these data algorithms to identify anomalies or threats to security. This solution provides an active early warning detection of security breaches from the network management perspective, along with timely response in the case of abnormal behaviour. It will ultimately result in improved cloud-based networks for IoT devices with regard to reliability, security, and performance. This solution can be a key factor in driving the mass implementation of IoT in various sectors. 2025 IEEE. Subject The topic of the resource considered; cybersecurity; explosive; identification; implementation; impossible; management; monitoring Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152605-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online