Advanced Persistent Threat Identification. Advanced Persistent Threats (APTs) represent one of the most critical challenges in modern cybersecurity. Their stealthy and evolving nature makes them particularly difficult to detect within the massive volume of system logs generated by enterprise environments. This thesis investigates the use of machine learning for APT detection from log data, comparing shallow classifiers, deep learning approaches, and a tactic-aware ensemble of fine-tuned BERT heads. \\ The experiments demonstrate that while shallow models can achieve competitive performance under random data splits, they fail to generalize when evaluated chronologically, underscoring their limited ability to adapt to the evolving behaviors characteristic of APT campaigns.