A Systematic Exploration of Evolutionary Computation for the Design of Hardware-oriented Non-cryptographic Hash Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.7877

@InProceedings{hassan:2024:GECCO,

• author = "Mujtaba Hassan and Jo Vliegen and Stjepan Picek and Nele Mentens",
• title = "A Systematic Exploration of Evolutionary Computation for the Design of Hardware-oriented Non-cryptographic Hash Functions",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference",
• year = "2024",
• editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
• pages = "1255--1263",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, non-cryptographic hash functions, bloom filters, FPGA, Real World Applications",
• isbn13 = "979-8-4007-0494-9",
• DOI = "doi:10.1145/3638529.3654009",
• size = "9 pages",
• abstract = "Non-cryptographic (NC) hash functions are crucial in high-speed search applications and probabilistic data structures (PDS) such as Bloom filters and Count-Min sketches for efficient lookups and counting. These operations necessitate execution at line rates to accommodate the high-speed demands of Terabit Ethernet networks, characterized by bandwidths exceeding 100 Gbps. Consequently, a growing inclination towards hardware platforms, particularly Field Programmable Gate Arrays (FPGAs), is evident in network security applications. Given the centrality of hash functions in these structures, any enhancements to their design carry substantial implications for overall system performance. However, hash functions must exhibit independence, uniform distribution, and hardware-friendly characteristics. In this work, we employ Genetic Programming (GP) with avalanche metrics as a fitness function to devise a hardware-friendly family of NC hash functions called the Evolutionary hash (E-hash). We provide a detailed experimental analysis to offer insights on primitive set combinations involving logical operations and diverse hyperparameter settings, encompassing variables such as the number of nodes, tree height, population size, crossover and mutation rate, tournament size, number of constants, and generations. Compared to existing state-of-the-art hardware-friendly hash functions, the proposed E-hash family exhibits an 8.4\% improvement in terms of operating frequency and throughput and 7.74\% in latency on FPGA.",
• notes = "GECCO-2024 RWA A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Mujtaba Hassan Jo Vliegen Stjepan Picek Nele Mentens

Citations