Paralelização em CUDA do algoritmo Aho-Corasick utilizando as hierarquias de memórias da GPU e nova compactação da Tabela de Transcrição de Estados

• Ano de defesa: 2017
• Autor(a) principal: Silva Júnior, José Bonifácio da
• Orientador(a): Moreno Ordonez, Edward David
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Sergipe
• Programa de Pós-Graduação: Pós-Graduação em Ciência da Computação
• País: Brasil
• Palavras-chave em Português: Ciência da computação; Computação de alto desempenho; Arquitetura de computador; Segurança da informação; GPUS; CUDA; Algoritmos de comparação de strings; Aho-Corasick; IDS; Hierarquia de memória da GPU; Técnicas de compactação
• Palavras-chave em Inglês: String matching algorithms; Aho-Corasick; GPU memory hierarchy; Compaction techniques
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://ri.ufs.br/handle/riufs/3353

The Intrusion Detection System (IDS) needs to compare the contents of all packets arriving at the network interface with a set of signatures for indicating possible attacks, a task that consumes much CPU processing time. In order to alleviate this problem, some researchers have tried to parallelize the IDS's comparison engine, transferring execution from the CPU to GPU. This This dissertation aims to parallelize the Brute Force and Aho-Corasick string matching algorithms and to propose a new compression of the State Transition Table of the Aho-Corasick algorithm in order to make it possible to use it in shared memory and accelerate the comparison of strings. The two algorithms were parallelized using the NVIDIA CUDA platform and executed in the GPU memories to allow a comparative analysis of the performance of these memories. Initially, the AC algorithm proved to be faster than the Brute Force algorithm and so it was followed for optimization. The AC algorithm was compressed and executed in parallel in shared memory, achieving a performance gain of 15% over other GPU memories and being 48 times faster than its serial version when testing with real network packets. When the tests were done with synthetic data (less random data) the gain reached 73% and the parallel algorithm was 56 times faster than its serial version. Thus, it can be seen that the use of compression in shared memory becomes a suitable solution to accelerate the processing of IDSs that need agility in the search for patterns.