In Bangladesh’s commercial banking sector, effective employee training is essential for improving performance, service quality, and overall organizational success. Authentic leadership, characterized by transparency, integrity, and a strong commitment to employee development, plays a crucial role in shaping training effectiveness. This study examines the influence of authentic leadership on workforce training outcomes, exploring how leaders' trustworthiness, ethical behavior, and employee engagement impact learning retention and skill application. Through a combination of qualitative and quantitative analysis, the research highlights the positive correlation between authentic leadership and enhanced employee training effectiveness. Findings suggest that fostering an authentic leadership culture leads to higher motivation, better knowledge transfer, and improved job performance, ultimately strengthening the competitive edge of Bangladesh’s banking industry.

This study addresses the challenge of detecting Advanced Persistent Threats (APTs) in corporate networks by developing a hybrid multi-modal detection framework. We combine traditional machine learning models, deep learning architectures, and transformer-based models to improve the detection of sophisticated and stealthy cyber threats. A comprehensive dataset, consisting of network traffic and event logs, was processed through rigorous data preprocessing, feature engineering, and model development. The results show that the hybrid ensemble model, integrating Gradient Boosting and Transformer-based architectures, outperforms all other models, achieving 98.7% accuracy, 98.3% precision, and 97.9% recall, while maintaining a false positive rate below 1%. The model demonstrated exceptional performance in real-world simulations, detecting over 98% of malicious activities. Our findings highlight the importance of combining the strengths of classical and advanced machine learning techniques for effective APT detection and mitigation, providing a reliable, scalable solution for real-time cybersecurity.