IWSHAP: Um Método de Seleção Incremental de Características para Redes CAN baseado em Inteligência Artificial Explicável (XAI)

• Felipe H. Scherer UNIPAMPA
• Felipe N. Dresch UNIPAMPA
• Silvio E. Quincozes UNIPAMPA / UFU
• Diego Kreutz UNIPAMPA
• Vagner E. Quincozes UFF

DOI: https://doi.org/10.5753/sbseg.2024.241780

Resumo

As redes CAN (Controller Area Network) são amplamente usadas na indústria automotiva e frequentemente alvo de ataques cibernéticos. A detecção desses ataques via aprendizado de máquina (AM) depende da seleção adequada de características para garantir o desempenho do modelo de predição. Este artigo propõe o IWSHAP, um novo método de seleção de características que combina o algorítimo Iterative Wrapper Subset Selection (IWSS) com os valores SHAP (SHapley Additive exPlanations). O principal objetivo é maximizar a performance do modelo de AM em um tempo reduzido. Os resultados indicam que IWSHAP consegue reduzir o número de características em até 99,17% e o tempo de execução em 98,3% comparado ao baseline.

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