Enhancing Cyber security Postures using Advanced Machine Learning Techniques: A Comprehensive Approach to Threat Detection and Mitigation

In the realm of cybersecurity, the effectiveness of threat detection and mitigation strategies is paramount. This study explores the enhancement of cybersecurity postures through the implementation of advanced machine learning techniques. Specifically, we investigate the use of Random Forest, Support Vector Machine (SVM), Stacking Classifier, and Convolutional Neural Networks (CNN) for identifying and countering cyber threats. Our approach integrates these algorithms to leverage their unique strengths, thereby improving accuracy and robustness in threat detection. By evaluating these techniques on diverse datasets, we aim to provide a comprehensive framework for strengthening cybersecurity defences. This research contributes to advancing the field of machine learning in cybersecurity, offering new insights into effective threat mitigation strategies.

Keywords: Random Forest, Support Vector Machine (SVM), Stacking Classifier, and Convolutional Neural Networks (CNN).