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Deep Learning Models for Cyber Security in IoT Networks: A Review

Author

Listed:
  • Kosrat Dlshad Ahmed

    (Information System Engineering, Erbil Polytechnic University, Erbil, Iraq)

  • Shavan Askar

    (Erbil Polytechnic University, Erbil, Iraq)

Abstract

The IoT systems and connectivity provide improved experience and improve the quality of service for the users in different perspectives. Recent development of the technological prospects and management of the sufficient aspects for the delivery of performance need to be ensured in this regard. The concept of IoT is related with the widely connected features, systems, data storage facilities, management processes, applications, devices, users, gateways, services and thousands of other elements. As the importance of IoT applications has been growing in recent times, the prospects for development and management are immense for the development opportunities. In recent times, cybersecurity and ensuring privacy for the users have attracted attention of the users. With growing popularity of the social media platforms, more and more people are becoming connected. With growing opportunity of connectivity, people need more secured space to connect. In this article, different aspects of the cybersecurity based on the deep learning models and analyzing the concepts of machine learning, understanding the concept of security and privacy, contributing to the development and management of cybersecurity etc. To demonstrate the understanding of cybersecurity in the IoT networks, effective deep learning models such as MLP, CNN, LSTP and a hybrid model of CNN and LSTP have been analyzed. To contribute to the learning process, future research opportunities have also been identified.

Suggested Citation

  • Kosrat Dlshad Ahmed & Shavan Askar, 2021. "Deep Learning Models for Cyber Security in IoT Networks: A Review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 61-70.
  • Handle: RePEc:aif:journl:v:5:y:2021:i:3:p:61-70
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    Citations

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    Cited by:

    1. Shavan Askar & Kurdistan Ali & Tarik A. Rashid, 2021. "Fog Computing Based IoT System: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 183-196.
    2. Shavan Askar & Faris Keti, 2021. "Performance Evaluation of Different SDN Controllers," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 67-80.
    3. Shavan Askar & Glena Aziz Qadir & Tarik A. Rashid, 2021. "SDN Based 5G VANET: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 131-147.
    4. Shavan Askar & Ibrahim Shamal Abdulkhaleq & Shahab Wahhab Kareem, 2021. "Blockchain systems: analysis, applications, & risks," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 163-173.
    5. Shavan Askar & Kosrat Dlshad Ahmed & Shahab Wahhab Kareem, 2021. "Deep learning Utilization in SDN Networks: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 174-182.
    6. Shavan Askar & Baydaa Hassan Husain & Tarik A. Rashid, 2021. "SDN Based Fog Computing: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 117-130.
    7. Shavan Askar & Zhwan Mohammed Khalid & Tarik A. Rashid, 2021. "Blockchain For Securing IoT Devices: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 209-224.
    8. Shavan Askar & Zhala Jameel Hamad & Shahab Wahhab Kareem, 2021. "Deep Learning and Fog Computing: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 197-208.
    9. Shavan Askar & Chnar Mustaf Mohammed & Shahab Wahhab Kareem, 2021. "Deep Learning in IoT systems: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 131-147.

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