Explainable AI for Intrusion Detection Systems: LIME and SHAP Applicability on Multi-Layer Perceptron

Machine learning-based systems have presented increasing learning performance, in a wide variety of tasks. However, the problem with some state-of-the-art models is their lack of transparency, trustworthiness, and explainability. To address this problem, eXplainable Artificial Intelligence (XAI) appeared. It is a research field that aims to make black-box models more understandable to humans. The research on this topic has increased in recent years, and many methods, such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) have been proposed. Machine learning-based Intrusion Detection Systems (IDS) are one of the many application domains of XAI. However, most of…

• UD
  Universidade de Coimbra

  Award: UID/CEC/00326/2020
• H2
  Horizon 2020 Framework Programme

  Awards: 101020259, 101020259(H2020-SU-DS02-2020)
• FP
  Fundação para a Ciência e a Tecnologia

  Awards: H2020, 00326, UID/CEC/00326/2020
• ES
  European Social Fund