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Enhanced Throughput and Accelerated Detection of Network Attacks Using a Membrane Computing Model Implemented on a GPU

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Advances in Nature-Inspired Computing and Applications

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Membrane computing (MC) is a versatile, nondeterministic, and maximally parallel computing model. We explore the advantages of MC parallelism to flag intrusive connection records in a set of network traffic using a graphic processing unit (GPU) that built on a parallelism platform with a single-program multiple data (SPMD) feature. We build a P system model for attack detection by combining some of the features of a recognizer P system and a tissue-like P system with symport rules. Most previous implementations for intrusion detection have been performed on sequential or minimally low parallel machines called a central processing unit (CPU), so the issue of large data handling has always been a major challenge. Using a massively parallel NVIDIA CUDA architecture, we were able to overcome this problem. Comparison of processing on a GPU and a CPU reveals an increase in average throughput of 50,000 packets/s and more than fivefold acceleration for the detection rate.

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Acknowledgements

This work has been supported by Fundamental Research Grant of Ministry of Higher Education of Malaysia (Grant Code : FRGS/1/2015/ICT04/UKM/02/3).

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Authors and Affiliations

  1. Computer Science Department, College of Science & Information Technology, Ijagun, Ogun State, Nigeria

    Rufai Kazeem Idowu

  2. Faculty of Information Science & Technology, Centre for Software Technology & Management, Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Ravie Chandren Muniyandi

Authors
  1. Rufai Kazeem Idowu
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  2. Ravie Chandren Muniyandi
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Correspondence to Ravie Chandren Muniyandi .

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Editors and Affiliations

  1. VIT Bhopal University, Bhopal, Madhya Pradesh, India

    Shishir Kumar Shandilya

  2. Sagar Institute of Research, Technology and Science, Bhopal, Madhya Pradesh, India

    Smita Shandilya

  3. Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

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Idowu, R.K., Muniyandi, R.C. (2019). Enhanced Throughput and Accelerated Detection of Network Attacks Using a Membrane Computing Model Implemented on a GPU. In: Shandilya, S., Shandilya, S., Nagar, A. (eds) Advances in Nature-Inspired Computing and Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-96451-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-96451-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96450-8

  • Online ISBN: 978-3-319-96451-5

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