The Role of Deep Learning in Network Intrusion Detection Systems: A Review

Abstract

This review synthesizes findings from several key studies focusing on the role of deep learning (DL) in network intrusion detection systems (NIDS). It highlights the growing importance of using DL techniques to enhance the detection of complex and evolving cyber threats. Traditional methods such as signature-based systems or anomalous systems often fail to meet the accuracy of modern attacks, prompting researchers to explore DLs to improve accuracy and adaptability. Several studies have demonstrated the effectiveness of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs) in classifying network traffic and identifying malicious activities. These deep learning models are particularly valuable because of their ability to automatically learn features from raw data, reducing the need for manual feature engineering. The review emphasizes the challenges in training DL models, including the need for large, labelled datasets and addressing issues associated with false positives and model interpretability. Despite these challenges, DL-based NIDS have shown significant improvements in real-time threat detection and mitigation rates. However, there is ongoing research to optimize these models for better performance, scalability, and generalizability across different network environments. Overall, the integration of deep learning into NIDS represents a promising frontier in combating increasingly sophisticated cyberattacks.