AI Article Synopsis

  • - The paper discusses the significance of deep learning (DL) models in intrusion detection, detailing commonly used datasets and methods in the field of cybersecurity for guiding future research efforts.
  • - It provides an overview of seven different DL-based intrusion detection models, including architectures like deep neural networks and transformers, highlighting their unique features and applications.
  • - The article emphasizes the need for further research in four specific areas to enhance the effectiveness and adaptability of DL-based systems against evolving cybersecurity threats, suggesting that advancements will lead to more accurate and efficient detection methods.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11509008PMC
http://dx.doi.org/10.3390/jimaging10100254DOI Listing

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