The research and application of intrusion detection algorithms based on deep learning (DL) have garnered significant interest due to the growing importance of securing computer systems against evolving cyber threats. This study offers an overview of DL techniques and architectures specifically tailored for intrusion detection tasks. It investigated the convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and generative adversarial networks (GANs), highlighting their respective strengths and applications in analyzing network traffic data. Furthermore, this work discussed commonly used intrusion detection datasets and preprocessing techniques essential for training DL models effectively. Looking ahead, this study proposed potential advancements in DL for intrusion detection, addressing scalability and resource constraints while considering ethical and privacy implications. By synthesizing current research findings and identifying future directions, this paper aims to contribute to the advancement of intrusion detection systems, enhancing the security posture of modern computer networks.

Chensha Wang, Yu Li, Lijing Liu. Research and Application of Intrusion Detection Algorithm Based on Deep Learning. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 3: 49-54. https://doi.org/10.25236/AJCIS.2024.070307.

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