Securing Enterprise Systems Using Deep Learning-Based Anomaly Detection Techniques

Abstract

Enterprise systems face an ever-expanding threat landscape, where sophisticated cyberattacks often bypass traditional signature-based defenses. This paper investigates the application of deep learning (DL) for anomaly detection as a robust mechanism to secure enterprise networks, servers, and endpoints. Unlike conventional methods, deep learning models—such as autoencoders, recurrent neural networks (RNNs), and graph neural networks (GNNs)—learn intricate patterns from vast amounts of system data, enabling the identification of zero-day attacks and subtle deviations from normal behavior. We examine the architecture of DL-based anomaly detection systems (ADS), including data preprocessing, feature extraction, model training, and real-time inference. The paper highlights key advantages, including reduced false positive rates, adaptability to evolving threats, and scalability across complex enterprise environments. Challenges such as adversarial attacks on DL models, high computational costs, and the need for large labeled datasets are critically analyzed. Finally, we propose a hybrid framework combining unsupervised and semi-supervised deep learning to overcome data-labeling constraints. Experimental results from recent case studies demonstrate detection accuracy exceeding 95% in realistic enterprise settings. This research concludes that deep learning-based anomaly detection is not a silver bullet but, when integrated with defense-in-depth strategies, significantly enhances enterprise security posture.