Abstract

Advanced Persistent Threats (APTs) represent some of the most sophisticated and evasive challenges confronted by modern cyber security. APTs target high‑value digital assets over prolonged time spans, leveraging stealth, polymorphism, and adaptive techniques that frequently evade signature‑based detection systems. This research explores the design, implementation, and evaluation of deep learning‑based models for the detection and mitigation of APTs in complex network environments. We investigate architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short‑term memory networks (LSTMs), and autoencoders to capture temporal, spatial, and behavioral patterns characteristic of APT activities. A hybrid detection framework is proposed that integrates feature extraction, attention mechanisms, and ensemble learning to enhance detection accuracy and reduce false positives. Performance is evaluated on benchmark intrusion detection datasets and real network traffic logs, focusing on metrics such as precision, recall, F1‑score, and detection latency. Results demonstrate that deep learning models significantly outperform traditional machine learning and signature‑based techniques in identifying stealthy threats while enabling automated mitigation through adaptive response strategies. The study concludes with insights into model scalability, operational deployment challenges, and future work on explainable deep security systems.