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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 7

Published on: July 2026

A REVIEW ON SUPERVISED MACHINE LEARNING TECHNIQUES FOR ENHANCING CYBER THREAT PREDICTION ACCURACY

Bandana Gupta Chandra Shekhar Gautam

AKS, University, Satna, MP, India

DOI:https://doi.org/10.55041/ijcope.v2i7.186

Article Status

Plagiarism Passed Peer Reviewed Open Access

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Abstract

The rapid expansion of digital infrastructures has increased the magnitude and sophistication of cyber threats, making timely and accurate threat prediction a foundational requirement for modern cyber-security systems. We use multiple algorithms—including Random Forest, Gradient Boosting Machines, and Deep Neural Networks—on benchmark intrusion-detection datasets and real-world enterprise log samples. Experimental results demonstrate that an ensemble-optimized model achieves improved predictive accuracy, reduced false positives, and enhanced generalization to unseen attack patterns. The study highlights important feature engineering techniques, model-optimization strategies, and deployment considerations for practical cyber-security environments. This research focuses on developing a supervised machine learning model to improve the accuracy of cyber threat prediction by leveraging historical and labeled cyber-security data.

Experimental analysis on a benchmark transaction dataset demonstrates that unsupervised models can achieve over 90% recall in detecting abnormal activities, providing a scalable and adaptive defense against evolving cyber threats in online banking.

How to Cite this Paper

Gupta, B. & Gautam, C. S. (2026). A Review on Supervised Machine Learning Techniques for Enhancing Cyber Threat Prediction Accuracy. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(7), 1-9. https://doi.org/10.55041/ijcope.v2i7.186

Gupta, Bandana, and Chandra Gautam. "A Review on Supervised Machine Learning Techniques for Enhancing Cyber Threat Prediction Accuracy." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 7, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i7.186.

Gupta, Bandana, and Chandra Gautam. "A Review on Supervised Machine Learning Techniques for Enhancing Cyber Threat Prediction Accuracy." International Journal of Creative and Open Research in Engineering and Management 02, no. 7 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i7.186.

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References


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Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Jul 20 2026
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