Deep Learning-Driven Intrusion Detection Systems: A Comprehensive Survey of Architectures, Performance Evaluation, and Research Challenges
TL;DR
A taxonomy of IDS architectures and a survey of recently proposed networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, GRUs, Autoencoders, GANs, Transformer-based, as well as hybrids are developed.
Abstract
The interconnected systems, cloud infrastructures, Internet of Things (IoT) devices, and large networks of today have increased the complexity and frequency of cyberattacks. There is now a growing requirement for intelligent and adaptive Intrusion Detection Systems (IDS). Classic signature-based and machine learning-based IDS methodologies usually fail to recognise advanced, evolving, or zero-day attacks due to their single-feature extraction capabilities and reliance on handcrafted rules. Accordingly, Deep Learning (DL) methods have surfaced with the ability of learning features automatically for better detection performance in other networks. This survey presents recent developments that have taken place in deep learning-based intrusion detection systems with supporting statistics from thirty-six representative works published across IoT, cloud, enterprise and SDN. This paper systematically develops a taxonomy of IDS architectures and a survey of recently proposed networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, GRUs, Autoencoders, GANs, Transformer-based, as well as hybrids. The performance of models has been evaluated using popular benchmark datasets such as NSL-KDD, UNSW-NB15, CICIDS2017, CICIDS2018, N-BaIoT and CICIoT2023. Many studies reported a detection accuracy more than 98%. Innovative methods such as feature selection, data augmentation, hyperparameter optimisation and spatial temporal learning have been discussed in detail. Although strides have been made yet numerous challenges still loom large, from class imbalance to high computational costs and limited interpretability of models. In the conclusion section, we highlight some of the important research challenges and future directions. Further, we discuss explainable artificial intelligence, adversarial robustness, federated learning, lightweight edge-deployable models, and Transformer-based IDS frameworks. According to this survey, this review will help researchers and practitioners in understanding the current developments and in designing the next generations intelligent intrusion detection systems.