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A Novel Cyber Security Model Using Deep Transfer Learning

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Abstract

Preventing attackers from interrupting or totally stop** critical services in cloud systems is a vital and challenging task. Today, machine learning-based algorithms and models are widely used, especially for the intelligent detection of zero-day attacks. Recently, deep learning methods that provide automatic feature extraction are designed to detect attacks automatically. In this study, we constructed a new deep learning model based on transfer learning for detecting and protecting cloud systems from malicious attacks. The developed deep transfer learning-based IDS converts network traffic into 2D preprocessed feature maps.Then the feature maps are processed with the transferred and fine-tuned convolutional layers of the deep learning model before the dense layer for detection and classification of traffic data. The results computed using the NSL-KDD test dataset reveal that the developed models achieve 89.74% multiclass and 92.58% binary classification accuracy. We performed another evaluation using only 20% of the training dataset as test data, and 80% for training. In this case, the model achieved 99.83% and 99.85% multiclass and binary classification accuracy, respectively.

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Data Availibility

The NSL-KDD dataset is accessible at: https://www.unb.ca/cic/datasets/nsl.html.

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Acknowledgements

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Authors and Affiliations

  1. Software Engineering Department, Sakarya University, Sakarya, Turkey

    Ünal Çavuşoğlu & Devrim Akgun

  2. Computer Engineering Department, Sakarya University of Applied Sciences, Sakarya, Turkey

    Selman Hizal

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  1. Ünal Çavuşoğlu
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  3. Selman Hizal
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Correspondence to Ünal Çavuşoğlu.

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Çavuşoğlu, Ü., Akgun, D. & Hizal, S. A Novel Cyber Security Model Using Deep Transfer Learning. Arab J Sci Eng 49, 3623–3632 (2024). https://doi.org/10.1007/s13369-023-08092-1

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