Mitigating Developed Persistent Threats (APTs) through Machine Learning-Based Intrusion Detection Systems: A Comprehensive Analysis

Abstract

Persistent threats (APTs) pose a significant challenge to cybersecurity due to their incredible evasive nature. Identification systems often fail to recognize APTs, resulting in significant data breaches and lost revenue. This study aims to solve this problem by developing a machine learning based intrusion detection system (IDS) specifically designed for APT detection This study aims to evaluate the performance of different machine learning algorithms for APT detection, IDS integration of these algorithms are efficient. The system includes integration and evaluation of system performance under real-world conditions. A major contribution of this research includes a comprehensive investigation of machine learning methods for APT recognition, IDS reprogramming, extensive empirical validation using real-world data Findings show that the proposed IDS greatly improves detection accuracy while reducing false positives for.