Enhancing Cybersecurity Through Machine Learning: A Comparative Analysis of Classifier Performance

In an era marked by the exponential growth of cyber threats, the imperative for robust cyber security measures has never been more pressing. With the proliferation of internet usage globally, cyber criminal activities such as DDoS attacks, data breaches, and credential compromises have surged, resulting in significant financial losses and reputational damage for organizations. Safeguarding sensitive information against these threats is paramount, necessitating innovative defense strategies. The study investigates the performance of various machine learning classifiers, including Random Forest, K Neighbors Classifier, Support Vector Machine (SVM), and Ensemble Model, for detecting network attacks. It proposes a method that combines the Random-Forest Model with the Recursive Feature Elimination (RFE) technique to identify relevant attributes from the NSL-KDD dataset. Comparative analysis with other machine learning models and research papers reveals that Random Forest surpasses existing cutting-edge technologies. Key measures such as accuracy, precision, recall, and F-measure are examined to provide insights into each classifier's effectiveness in detecting different types of network intrusions. These findings contribute to enhancing intrusion detection systems and fortifying cyber security defenses against evolving threats in network environments.