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In the rapidly evolving landscape of cybersecurity, Intrusion Detection Systems (IDS) play a crucial role in identifying and mitigating malicious activities within network environments. While traditional IDS methods have proven effective, they often struggle to cope with the sophistication and volume of modern cyber threats. This survey investigates the integration of optimization algorithms and hybrid deep learning techniques to enhance the efficacy of IDS. By combining optimization with hybrid architectures such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, we can leverage their strengths in managing complex data patterns and temporal dependencies. This paper reviews 25 research studies focused on intrusion detection and examines the potential of computer-assisted methods for improving detection performance. The findings suggest that integrating optimization with hybrid deep learning techniques offers a promising pathway for advancing IDS capabilities, thereby ensuring more robust network security in the face of ever-evolving cyber threats..

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An Efficient Intrusion Detection System Using Deep Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • P. R. Pameela Rani,
  • S. Vidhya

摘要

In the rapidly evolving landscape of cybersecurity, Intrusion Detection Systems (IDS) play a crucial role in identifying and mitigating malicious activities within network environments. While traditional IDS methods have proven effective, they often struggle to cope with the sophistication and volume of modern cyber threats. This survey investigates the integration of optimization algorithms and hybrid deep learning techniques to enhance the efficacy of IDS. By combining optimization with hybrid architectures such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, we can leverage their strengths in managing complex data patterns and temporal dependencies. This paper reviews 25 research studies focused on intrusion detection and examines the potential of computer-assisted methods for improving detection performance. The findings suggest that integrating optimization with hybrid deep learning techniques offers a promising pathway for advancing IDS capabilities, thereby ensuring more robust network security in the face of ever-evolving cyber threats..