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Adaptive Threat Detection using Lightweight Hybrid Learning in Cloud-Scale Environments

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Adaptive Threat Detection using Lightweight Hybrid Learning in Cloud-Scale Environments


Vasudev Karthik Ravindran | Shivaraj Yanamandram Kuppuraju | Vineet Baniya



Vasudev Karthik Ravindran | Shivaraj Yanamandram Kuppuraju | Vineet Baniya "Adaptive Threat Detection using Lightweight Hybrid Learning in Cloud-Scale Environments" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3, June 2025, pp.1293-1300, URL: https://www.ijtsrd.com/papers/ijtsrd97113.pdf

This research presents a novel adaptive threat detection framework that leverages lightweight hybrid learning to enhance cybersecurity in cloud-scale environments. Addressing the limitations of traditional intrusion detection systems and standalone machine learning models, the proposed approach integrates supervised and unsupervised learning techniques within a resource-efficient, scalable architecture. The model is designed to detect both known and unknown threats by combining classification capabilities with anomaly detection, further strengthened by a continuous feedback loop for real-time adaptability. Experiments conducted using benchmark datasets such as CICIDS2017 and UNSW-NB15, along with simulated cloud traffic, demonstrate that the proposed system outperforms existing solutions in terms of accuracy, precision, recall, F1-score, and AUC, while maintaining low latency and high scalability. Deployed within a containerized environment to emulate real-world conditions, the model showcases excellent performance in handling dynamic workloads, evolving attack patterns, and compliance-sensitive deployments. This study establishes a practical, efficient, and future-ready framework for intelligent threat detection, contributing significantly to the advancement of secure cloud computing.

Adaptive threat detection, hybrid learning, cloud security, machine learning, anomaly detection


IJTSRD97113
Volume-9 | Issue-3, June 2025
1293-1300
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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