Nov 28, 2018 · In this paper we present an approach to generate explanations for incorrect classifications made by data-driven Intrusion Detection Systems (IDSs).
Nov 28, 2018 · In this paper we present an approach to generate explanations for incorrect classifications made by data-driven Intrusion Detection Systems ( ...
This survey reviews the state of the art in explainable AI (XAI) for IDS, its current challenges, and discusses how these challenges span to the design of ...
Oct 30, 2018 · In this paper we present an approach to generate explanations for incorrect classifications made by data-driven Intrusion Detection Systems ( ...
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In this paper, we perform experiments to show that generic removal/insertion XAI evaluation methods are not suitable for deepfake detection models.
Feb 29, 2024 · The paper provides an overview of current methods, challenges, and opportunities in X-IDS, making it a valuable resource for researchers and.
This survey provides a comprehensive overview of research concerning adversarial attacks on explanations of machine learning models, as well as fairness metrics ...
Sep 15, 2021 · In this paper, we propose an adversarial attack detection framework in machine learning-based explainable AI intrusion detection systems. The ...
Deep learning techniques are widely used for anomaly-based intrusion detection. The technique gives a satisfactory result for known attacks but for zero-day ...
This paper developed an adversarial robust and explainable network intrusion detection system based on deep learning by applying adversarial training and ...