A Review on Supervised Machine Learning Techniques for Enhancing Cyber Threat Prediction Accuracy

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.