HYBRID MULTI-MODAL DETECTION FRAMEWORK FOR ADVANCED PERSISTENT THREATS IN CORPORATE NETWORKS USING MACHINE LEARNING AND DEEP LEARNING

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.