Effective Cyber Threat Detection Through Machine Learning Algorithms

the digital world today, with the rapidly evolving threats against information systems, has required quite sophisticated mechanisms for their detection. This work evaluates the efficiency of using machine learning algorithms as cyber threat detectors through the comparison of such models as Random Forest, Isolation Forest, Bagging Classifier, and Support Vector Machine. In the research, the performance of these algorithms was estimated on a fully detailed dataset of network communication scenarios for different classes of cyber threats with the purpose of identification and mitigation. Results indicated that Random Forest performed best, providing an accuracy of 93.2% with very good precision and recall values, while the Bagging Classifier also worked quite well. While Isolation Forest and Support Vector Machine were pretty effective in this respect, they were less than optimal compared to the ensemble methods. The findings stress huge machine learning gains to cyber threat detection and underline ensemble models in offering very accurate and reliable solutions for security. Future research includes exploring hybrid models, real-time detection capabilities, further feature engineering, adaptability, scalability, and model explainability.