International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Strategic Innovations and Future Directions in Deep Learning -Based Intrusion Detection Models

Author(s) Dr. Jayeshkumar Madhubhai Patel
Country India
Abstract With the advancement of deep learning (DL) technology, intrusion detection models based on deep learning have become a significant research topic in the field of cyber security. This paper reviews the datasets commonly employed in such research, laying the groundwork for subsequent studies and analyses. The following section collates the most prevalent data preprocessing methods and feature engineering techniques within intrusion detection, while outlining seven deep learning-based intrusion detection models: deep auto encoders, deep belief networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformer models. Each model is evaluated from multiple perspectives, emphasising its unique architecture and application scenarios within cyber security. Furthermore, this paper extends the scope to include two large-scale prediction models: methods integrating BERT and GPT series for auxiliary penetration detection. These models leverage the advantages of the Transformer architecture and attention mechanisms, demonstrating exceptional performance in understanding and processing sequential data. Building upon these findings, this paper adopts a forward-looking perspective to explore future research directions, identifying four core research domains.
Keywords Artificial Intelligence, Deep Learning, Engineering, Systematic Literature Review, Neural Networks, Machine learning algorithm, Deep neural network architectures and convolution neural network
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-28
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.65104
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E-ISSN 2582-2160
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