  The increase in network attacks has necessitated the development of robust
and efficient intrusion detection systems (IDS) capable of identifying
malicious activities in real-time. In the last five years, deep learning
algorithms have emerged as powerful tools in this domain, offering enhanced
detection capabilities compared to traditional methods. This review paper
studies recent advancements in the application of deep learning techniques,
including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN),
Deep Belief Networks (DBN), Deep Neural Networks (DNN), Long Short-Term Memory
(LSTM), autoencoders (AE), Multi-Layer Perceptrons (MLP), Self-Normalizing
Networks (SNN) and hybrid models, within network intrusion detection systems.
we delve into the unique architectures, training models, and classification
methodologies tailored for network traffic analysis and anomaly detection.
Furthermore, we analyze the strengths and limitations of each deep learning
approach in terms of detection accuracy, computational efficiency, scalability,
and adaptability to evolving threats. Additionally, this paper highlights
prominent datasets and benchmarking frameworks commonly utilized for evaluating
the performance of deep learning-based IDS. This review will provide
researchers and industry practitioners with valuable insights into the
state-of-the-art deep learning algorithms for enhancing the security framework
of network environments through intrusion detection.
