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International Conference on the Applications of Evolutionary Computation (Part of EvoStar)

EvoApplications 2023: Applications of Evolutionary Computation pp 302–318Cite as

Evolving Non-cryptographic Hash Functions Using Genetic Programming for High-speed Lookups in Network Security Applications

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13989))

Abstract

Non-cryptographic (NC) hash functions are the core part of many networking and security applications such as traffic flow monitoring and deep packet inspection. For these applications, speed is more important than strong cryptographic properties. In Terabit Ethernet networks, the speed of the hash functions can have a significant impact on the overall performance of the system when it is required to process the packets at a line rate. Hence, improving the speed of hash functions can have a significant impact on the overall performance of such architectures. Designing a good hash function is a challenging task because of the highly non-linear and complex relationship between input and output variables. Techniques based on Evolutionary Computation (EC) excel in addressing such challenges. In this paper, we propose novel fast non-cryptographic hash functions using genetic programming, and we call the resulting hash functions the GPNCH (Genetic Programming-based Non-Cryptographic Hash) family. We choose to employ avalanche metrics as a fitness function because the networking and security applications we consider require hash functions to be uniform and independent. We evaluate the performance of GPNCH functions on FPGA and compare the delay, throughput, and resource occupation with the state-of-the-art NC hash functions that satisfy the avalanche criteria. We show that GPNCH functions outperform the other algorithms in terms of latency, operating frequency, and throughput at the modest cost of hardware resources.

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Acknowledgement

This work is supported by the ESCALATE project, funded by FWO (G0E0719N) and SNSF (200021L_182005). This work is also supported by Cybersecurity Research Flanders (VR20192203) and the Higher Education Commission, Pakistan.

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Authors and Affiliations

  1. imec-COSIC/ES &S, ESAT, KU Leuven, Leuven, Belgium

    Mujtaba Hassan, Arish Sateesan, Jo Vliegen & Nele Mentens

  2. LIACS, Leiden University, Leiden, The Netherlands

    Nele Mentens

  3. Digital Security Group, Radboud University, Nijmegen, The Netherlands

    Stjepan Picek

Authors
  1. Mujtaba Hassan
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  2. Arish Sateesan
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  3. Jo Vliegen
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  4. Stjepan Picek
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  5. Nele Mentens
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Corresponding author

Correspondence to Mujtaba Hassan .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    João Correia

  2. University of York, York, UK

    Stephen Smith

  3. Al Hussein Technical University, Amman, Jordan

    Raneem Qaddoura

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Hassan, M., Sateesan, A., Vliegen, J., Picek, S., Mentens, N. (2023). Evolving Non-cryptographic Hash Functions Using Genetic Programming for High-speed Lookups in Network Security Applications. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_20

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  • DOI: https://doi.org/10.1007/978-3-031-30229-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30228-2

  • Online ISBN: 978-3-031-30229-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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