https://github.com/nxhoang56/explainable-anomaly-detection-forindustrial-control-system-cybersecurity
공식 코드Explainable Anomaly Detection for Industrial Control System Cybersecurity
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XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Explainable Anomaly Detection for Industrial Control System Cybersecurity
Shapelet-Based Counterfactual Explanations for Multivariate Time Series
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models
Challenges facing the explainability of age prediction models: case study for two modalities
Explainable AI for Comparative Analysis of Intrusion Detection Models
CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations
AS-XAI: Self-supervised Automatic Semantic Interpretation for CNN
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation
XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
Explainable AI for Bioinformatics: Methods, Tools, and Applications
Explainable Artificial Intelligence for Improved Modeling of Processes
A Co-design Study for Multi-Stakeholder Job Recommender System Explanations
Reproducibility study of "LICO: Explainable Models with Language-Image Consistency"
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions
Pantypes: Diverse Representatives for Self-Explainable Models
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.