Study on Real-Time Data Integrity and Security Module Using Machine Learning
Year: 2025
Type: article
Abstract: The rapid increase of interconnected virtual ecosystems, pushed by means of IoT devices, cloud infrastructures, and software program-described Networks (SDNs), has amplified the call for for sturdy actual-time information safety and integrity. This studies introduces a real-Time data Integrity and security Module (RTDISM) that leverages system getting to know (ML) to tackle these challenges. by way of integrating SDN architectures, light-weight federated getting to know, and adaptive authentication protocols, the RTDISM provides a scalable, multi-layered defense gadget suitable for diverse environments. Key capabilities include adaptive ML-pushed risk mitigation, low-latency responses, and efficient aid usage. The module is particularly proper for vital domain names together with healthcare, commercial automation, and self reliant systems. Innovat ions include SDN-primarily based security for improved network flexibility, light-weight ML models for aid optimization, and federated getting to know for decentralized, privateness-maintaining operations. Experimental opinions demonstrate the RTDISM's superior overall performance in accuracy, reaction time, and useful resource performance in comparison to present solutions, organising it as a benchmark for subsequent- technology cybersecurity structures.
Authors: Greeva Dinesh Kumar Patel, Kartikeydheer Srivastava, Mansi Mehta
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6912046931
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