Performance Analysis of Hybrid Cloud Intrusion ‎DetectionModel by Using Supervised Machine Learning ‎BasedClassification Algorithms

  • Authors

    • Mr. D. Rajagopal Assistant Professor& Research Scholar, Department of Computer Science, Vivekanandha College of Arts and ‎Sciences for Women (Autonomous)(Affiliated to Periyar University), Tiruchengode, India
    • Dr. K. Padmanabhan Professor & Research Supervisor, Department of Computer Science, Vivekanandha College of Arts and ‎Sciences for Women (Autonomous)(Affiliated to Periyar University), Tiruchengode, India
    https://doi.org/10.14419/mrn3dh08

    Received date: June 23, 2025

    Accepted date: July 25, 2025

    Published date: August 15, 2025

  • Cloud IDS; Decision Trees (DT); K-Nearest Neighbors (KNN); Linear Regression (LR); Machine Learning; Neural ‎Networks (NN); Random Forests (RF)‎.

  • Abstract

    Cloud Computing refers to an Internet-based infrastructure that delivers shared resources, software, and ‎information to computers and other devices on an on-demand basis. However, it faces numerous security ‎challenges, including issues related to availability, data confidentiality, integrity, and access control. ‎Additionally, Cloud Computing is vulnerable to various conventional attacks. Traditional security systems ‎are inadequate to protect Cloud services from these diverse threats. In the realm of Cloud Computing, ‎Intrusion Detection refers to the identification and management of unauthorized access, harmful actions, ‎and potential security threats. Intrusion Detection Systems (IDS) serve as security mechanisms that monitor ‎network traffic and event logs to detect any anomalous activities. The Cloud Computing (CC) environment ‎necessitates the implementation of specific Intrusion Detection Systems to safeguard each machine from ‎potential attacks. Machine Learning and Deep Learning algorithms enhance the accuracy of Intrusion ‎Detection Systems over time, leading to a decrease in both false positives and false negatives. In the hybrid ‎phase, the implementation of Naïve Bayes and Decision Tree algorithms resulted in an impressive ‎accuracy rate of 99.71%. Future research should explore additional combinations of hybrid models to ‎achieve greater efficiency across all performance metrics‎.

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  • How to Cite

    Rajagopal, M. D. . (2025). Performance Analysis of Hybrid Cloud Intrusion ‎DetectionModel by Using Supervised Machine Learning ‎BasedClassification Algorithms (D. K. . Padmanabhan , Trans.). International Journal of Basic and Applied Sciences, 14(4), 439-444. https://doi.org/10.14419/mrn3dh08