Advanced Persistent Threat Detection Through Multi-Layered Machine Learning: The MLADA Framework

Advanced Persistent Threats (APTs) represent one of the most sophisticated and dangerous cybersecurity challenges of our time. These stealthy, long-term attacks are designed to remain undetected while continuously extracting sensitive information from target systems. This paper presents a comprehensive analysis of APT characteristics, detection methodologies, and proposes a novel machine learning-based algorithm for APT detection. Our approach combines behavioral analysis, network traffic monitoring, and anomaly detection to identify potential APT activities. The proposed algorithm demonstrates improved detection rates while maintaining low false positive rates, making it suitable for real-world deployment in enterprise environments.