Wu Yuqiang, Zou Bailin, Cao Yifei
College of Information and Technology, Nanjing Police University, Nanjing 210023, China.
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China.
J Imaging. 2024 Oct 14;10(10):254. doi: 10.3390/jimaging10100254.
With the advancement of deep learning (DL) technology, DL-based intrusion detection models have emerged as a focal point of research within the domain of cybersecurity. This paper provides an overview of the datasets frequently utilized in the research. This article presents an overview of the widely utilized datasets in the research, establishing a basis for future investigation and analysis. The text subsequently summarizes the prevalent data preprocessing methods and feature engineering techniques utilized in intrusion detection. Following this, it provides a review of seven deep learning-based intrusion detection models, namely, deep autoencoders, deep belief networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers. Each model is examined from various dimensions, highlighting their unique architectures and applications within the context of cybersecurity. Furthermore, this paper broadens its scope to include intrusion detection techniques facilitated by the following two large-scale predictive models: the BERT series and the GPT series. These models, leveraging the power of transformers and attention mechanisms, have demonstrated remarkable capabilities in understanding and processing sequential data. In light of these findings, this paper concludes with a prospective outlook on future research directions. Four key areas have been identified for further research. By addressing these issues and advancing research in the aforementioned areas, this paper envisions a future in which DL-based intrusion detection systems are not only more accurate and efficient but also better aligned with the dynamic and evolving landscape of cybersecurity threats.
随着深度学习（DL）技术的进步，基于深度学习的入侵检测模型已成为网络安全领域研究的焦点。本文概述了该研究中经常使用的数据集，为未来的调查和分析奠定基础。随后，本文总结了入侵检测中普遍使用的数据预处理方法和特征工程技术。在此之后，本文回顾了七种基于深度学习的入侵检测模型，即深度自动编码器、深度信念网络、深度神经网络、卷积神经网络、循环神经网络、生成对抗网络和变换器。从各个维度对每个模型进行了考察，突出了它们在网络安全背景下的独特架构和应用。此外，本文将范围扩大到包括由以下两种大规模预测模型推动的入侵检测技术：BERT系列和GPT系列。这些模型利用变换器和注意力机制的力量，在理解和处理序列数据方面展现出了卓越的能力。鉴于这些发现，本文最后对未来的研究方向进行了前瞻性展望。确定了四个关键的进一步研究领域。通过解决这些问题并推动上述领域的研究，本文设想了一个未来，在这个未来中，基于深度学习的入侵检测系统不仅更加准确和高效，而且能更好地适应网络安全威胁动态演变的形势。
