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Optimized Edge-cCCN Based Model for the Detection of DDoS Attack in IoT Environment

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Edge Computing – EDGE 2023 (EDGE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14205))

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Abstract

In the context of the Internet of Things (IoT), safeguarding against Distributed Denial of Service (DDoS) attacks is critical. This paper introduces an Optimized Edge-cCNN (Convolutional Neural Network) Model designed for robust DDoS detection in IoT environments. The model employs two specialized CNN layers to identify distinct DDoS attack types. To enhance its performance, we utilize the Cuckoo Search algorithm to fine-tune hyperparameters effectively. Our approach demonstrates superior accuracy compared to existing methods while remaining lightweight, making it suitable for resource-constrained edge devices. Through rigorous evaluation, our model exhibits its effectiveness in real-time DDoS threat mitigation. The Optimized Edge-cCNN Model presents an innovative solution for enhancing IoT security, combining deep learning and optimization techniques to combat evolving DDoS attacks effectively.

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Acknowledgement

This research work is supported by National Science and Technology Council (NSTC), Taiwan Grant No. NSTC112-2221-E-468-008-MY3.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan

    Brij B. Gupta

  2. Lebanese American University, Beirut, 1102, Lebanon

    Brij B. Gupta

  3. Ronin Institute, Montclair, NJ, 07043, USA

    Akshat Gaurav

  4. School of Science and Technology, Hong Kong Metropolitan University, Kowloon, Hong Kong

    Kwok Tai Chui

  5. Department of Business Administration, Asia University, Taichung, Taiwan

    Varsha Arya

  6. Insights2Techinfo, New Delhi, India

    Varsha Arya

Authors
  1. Brij B. Gupta
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  2. Akshat Gaurav
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  3. Kwok Tai Chui
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  4. Varsha Arya
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Corresponding author

Correspondence to Brij B. Gupta .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Jun Feng

  2. Deakin University, Burwood, VIC, Australia

    Frank Jiang

  3. Georgia Institute of Technology, Atlanta, GA, USA

    Min Luo

  4. Shenzhen Entrepreneurship and Innovation Federation, Shenzhen, China

    Liang-Jie Zhang

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Gupta, B.B., Gaurav, A., Chui, K.T., Arya, V. (2024). Optimized Edge-cCCN Based Model for the Detection of DDoS Attack in IoT Environment. In: Feng, J., Jiang, F., Luo, M., Zhang, LJ. (eds) Edge Computing – EDGE 2023 . EDGE 2023. Lecture Notes in Computer Science, vol 14205. Springer, Cham. https://doi.org/10.1007/978-3-031-51826-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-51826-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51825-6

  • Online ISBN: 978-3-031-51826-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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