Authors: Siti Amina
Abstract: Deep learning-based intrusion detection systems (IDS) have emerged as a transformative approach for securing enterprise networks in the face of increasingly sophisticated cyber threats. Traditional signature-based and rule-based IDS solutions struggle to detect zero-day attacks, polymorphic malware, and advanced persistent threats due to their reliance on predefined patterns. In contrast, deep learning models offer the ability to automatically learn hierarchical feature representations from large-scale network traffic data, enabling improved detection accuracy and adaptability. This review examines the evolution, methodologies, and practical implementation of deep learning-based IDS in enterprise environments. It highlights the role of architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and transformer-based models in identifying anomalous and malicious activities. The study further explores data preprocessing techniques, feature engineering, and benchmark datasets commonly used for training and evaluation. Key challenges, including data imbalance, model interpretability, computational overhead, and real-time deployment constraints, are critically analyzed. Additionally, the integration of deep learning IDS with emerging technologies such as cloud computing, edge computing, and software-defined networking (SDN) is discussed. The review concludes by outlining future research directions focused on improving scalability, explainability, and resilience against adversarial attacks. Overall, deep learning-based IDS represent a promising paradigm shift in enterprise cybersecurity, offering intelligent, adaptive, and proactive defense mechanisms.
DOI: https://doi.org/10.5281/zenodo.19491764
