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An efficient hybrid deep learning approach for internet security

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  • Ertam, Fatih

Abstract

Nowadays, internet is mostly used communication tool worldwide. However, the major problem of the internet is to provide security. To provide internet security, many researches and papers have been suggested about information and network security. The commonly used system against network attacks is firewalls.

Suggested Citation

  • Ertam, Fatih, 2019. "An efficient hybrid deep learning approach for internet security," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119314293
    DOI: 10.1016/j.physa.2019.122492
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    References listed on IDEAS

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    1. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    2. Timothy P. Lillicrap & Daniel Cownden & Douglas B. Tweed & Colin J. Akerman, 2016. "Random synaptic feedback weights support error backpropagation for deep learning," Nature Communications, Nature, vol. 7(1), pages 1-10, December.
    3. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
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    Cited by:

    1. Ramezani, Zahra & Pourdarvish, Ahmad, 2021. "Transfer learning using Tsallis entropy: An application to Gravity Spy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).

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