Ensemble Learning-Based Methods for Detecting Advanced Persistent Threats

This paper presents a theoretical framework examining the use of ensemble learning methods for detecting Advanced Persistent Threats (APTs) in cybersecurity. It analyzes why ensemble learning could offer advantages over traditional detection methods and neural networks, focusing on reduced complexity, faster training, and lower resource requirements. The work establishes a theoretical foundation for implementing ensemble learning approaches in APT detection systems.