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Deep Learning-Based Intrusion Detection Model for Network Security

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Intelligent Computing and Networking (IC-ICN 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 699))

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Abstract

Since it serves as a potent means of network security defence, intrusion detection technology is an essential component of the network security system. As the Internet has grown quickly, so too have network data volumes and threats, which are now more sophisticated and diversified. Modern intrusion detection equipment cannot reliably recognize different types of attacks. A CBL_DDQN intrusion detection model based on an upgraded double deep Q network is suggested based on deep reinforcement learning to address the imbalance of regular traffic and attack traffic data in the actual network environment as well as the low detection rate of attack traffic. This model integrates the feedback learning and policy-generating methods of deep reinforcement learning with a one-dimensional convolutional neural network and a bidirectional long-term, short-term memory network to train agents to attack different types of samples. Classification, to some extent, lessens the reliance on data labels during model training. The Borderline-SMOTE algorithm reduces data imbalance, thereby improving the detection rate of rare attacks. The NSL KDD and UNSW NB15 data sets are used to assess the model’s efficacy. The findings demonstrate that the model has performed well with respect to the three indices of accuracy, precision, and recall, and the detection effect is significantly superior to Adam BNDNN, KNN, SVM, etc. The detection method is an efficient network intrusion detection model.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

    Sagar Dhanraj Pande

  2. Data Science and Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India

    Govinda Rajulu Lanke

  3. University Centre for Research and Development Chandigarh University, Mohali, 140413, Punjab, India

    Mukesh Soni

  4. Bharati Vidyapeeth (Deemed to Be University), Institute of Management, Kolhapur, India

    Mukund Anant Kulkarni

  5. College of Engineering, Southern Luzon State University, Lucban, Quezon, Philippines

    Renato R. Maaliw

  6. Department of Management, Lovely Professional University, Phagwara, India

    Pavitar Parkash Singh

Authors
  1. Sagar Dhanraj Pande
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  2. Govinda Rajulu Lanke
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  3. Mukesh Soni
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  4. Mukund Anant Kulkarni
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  5. Renato R. Maaliw
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  6. Pavitar Parkash Singh
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Corresponding author

Correspondence to Sagar Dhanraj Pande .

Editor information

Editors and Affiliations

  1. Aurel Vlaicu University of Arad, Arad, Arad, Romania

    Valentina Emilia Balas

  2. NIT, Bhopal, India

    Vijay Bhaskar Semwal

  3. Thakur College of Engineering and Technology, Mumbai, Maharashtra, India

    Anand Khandare

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pande, S.D., Lanke, G.R., Soni, M., Kulkarni, M.A., Maaliw, R.R., Singh, P.P. (2023). Deep Learning-Based Intrusion Detection Model for Network Security. In: Balas, V.E., Semwal, V.B., Khandare, A. (eds) Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_27

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