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Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network

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Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The climate has changed absolutely in every area in just a few years as digitized, making high-speed internet service a significant need in the future. Future Internet is supposed to face exponential growth in traffic, and highly complicated infrastructure, threatening to make conventional NTC approaches unreliable and even counterproductive. In recent days, AI Stimulated state-of-the-art breakthroughs with the ability to tackle extensive and multifarious challenges, and the network community is initiated by considering the NTC prototype from legacy rule-based towards a novel AI-based. Design and execution are applied to interdisciplinary become more essential. A smart home network supports various applications and smart devices within the proposed work, including e-health devices, regular computing devices, and home automation devices. Many devices accessible through the Internet by Home GateWay for Congestion (HGC) in a smart home. Throughout this paper, a Software-Defined Network Home GateWay for Congestion (SDNHGC) architecture for improved management of remote smart home networks and protection of the significant network's SDN controller. It enables effective network capacity regulation, focused on real-time traffic analysis and core network resource allocation. It cannot control the Network in dispersed smart homes. Our innovative SDNHGC expands power across the connectivity network, a smart home network enabling improved end-to-end monitoring of networks. The planned SDNHGC directly will gain centralized device identification by classifying traffic through a smart home network. Several of the current traffic classifications approach, checking deep packets, cannot have this real-time device knowledge for encrypted data to solve this issue.

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Authors and Affiliations

  1. Department Management, Universitas Kristen Petra, Surabaya, Indonesia

    Roy Setiawan

  2. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522502, India

    Ramakoteswara Rao Ganga

  3. Department of Computer Science and Engineering, Paavai Engineering College, Namakkal, Tamil Nadu, 637018, India

    Priya Velayutham

  4. Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India

    Kumaravel Thangavel

  5. Department of Mathematics, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, 473226, India

    Dilip Kumar Sharma

  6. Department of Information Technology, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, 635109, India

    Regin Rajan

  7. Department of Computer Science, Wenzhou-Kean University, Wenzhou, 325060, Zhejiang, China

    Sujatha Krishnamoorthy

  8. Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, 627152, India

    Sudhakar Sengan

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  1. Roy Setiawan
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  2. Ramakoteswara Rao Ganga
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  3. Priya Velayutham
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  4. Kumaravel Thangavel
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Correspondence to Ramakoteswara Rao Ganga, Priya Velayutham or Sudhakar Sengan.

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Setiawan, R., Ganga, R.R., Velayutham, P. et al. Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network. Wireless Pers Commun 127, 749–765 (2022). https://doi.org/10.1007/s11277-021-08403-5

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  • DOI: https://doi.org/10.1007/s11277-021-08403-5

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