Explainable Unsupervised Machine Learning for Cyber-Physical Systems
Cyber-Physical Systems (CPSs) play a critical role in our modern infrastructure due to their capability to connect computing resources with physical systems. As such, topics such as reliability, performance, and security of CPSs continue to receive increased attention from the research community. CP...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9536751/ |
| _version_ | 1818676946171592704 |
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| author | Chathurika S Wickramasinghe Kasun Amarasinghe Daniel L. Marino Craig Rieger Milos Manic |
| author_facet | Chathurika S Wickramasinghe Kasun Amarasinghe Daniel L. Marino Craig Rieger Milos Manic |
| author_sort | Chathurika S Wickramasinghe |
| collection | DOAJ |
| description | Cyber-Physical Systems (CPSs) play a critical role in our modern infrastructure due to their capability to connect computing resources with physical systems. As such, topics such as reliability, performance, and security of CPSs continue to receive increased attention from the research community. CPSs produce massive amounts of data, creating opportunities to use predictive Machine Learning (ML) models for performance monitoring and optimization, preventive maintenance, and threat detection. However, the “black-box” nature of complex ML models is a drawback when used in safety-critical systems such as CPSs. While explainable ML has been an active research area in recent years, much of the work has been focused on supervised learning. As CPSs rapidly produce massive amounts of unlabeled data, relying on supervised learning alone is not sufficient for data-driven decision making in CPSs. Therefore, if we are to maximize the use of ML in CPSs, it is necessary to have explainable unsupervised ML models. In this paper, we outline how unsupervised explainable ML could be used within CPSs. We review the existing work in unsupervised ML, present initial desiderata of explainable unsupervised ML for CPS, and present a Self-Organizing Maps based explainable clustering methodology which generates global and local explanations. We evaluate the fidelity of the generated explanations using feature perturbation techniques. The results show that the proposed method identifies the most important features responsible for the decision-making process of Self-organizing Maps. Further, we demonstrated that explainable Self-Organizing Maps are a strong candidate for explainable unsupervised machine learning by comparing its model capabilities and limitations with current explainable unsupervised methods. |
| first_indexed | 2024-12-17T08:51:33Z |
| format | Article |
| id | doaj.art-2f428834fcf34739a8bdd7ef30bd2e84 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T08:51:33Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2f428834fcf34739a8bdd7ef30bd2e842022-12-21T21:56:04ZengIEEEIEEE Access2169-35362021-01-01913182413184310.1109/ACCESS.2021.31123979536751Explainable Unsupervised Machine Learning for Cyber-Physical SystemsChathurika S Wickramasinghe0https://orcid.org/0000-0002-3333-5101Kasun Amarasinghe1Daniel L. Marino2https://orcid.org/0000-0002-8686-4752Craig Rieger3https://orcid.org/0000-0002-3198-8838Milos Manic4https://orcid.org/0000-0003-1484-7678Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USACarnegie Mellon University, Pittsburgh, PA, USADepartment of Computer Science, Virginia Commonwealth University, Richmond, VA, USAIdaho National Laboratory (INL), Idaho Falls, ID, USADepartment of Computer Science, Virginia Commonwealth University, Richmond, VA, USACyber-Physical Systems (CPSs) play a critical role in our modern infrastructure due to their capability to connect computing resources with physical systems. As such, topics such as reliability, performance, and security of CPSs continue to receive increased attention from the research community. CPSs produce massive amounts of data, creating opportunities to use predictive Machine Learning (ML) models for performance monitoring and optimization, preventive maintenance, and threat detection. However, the “black-box” nature of complex ML models is a drawback when used in safety-critical systems such as CPSs. While explainable ML has been an active research area in recent years, much of the work has been focused on supervised learning. As CPSs rapidly produce massive amounts of unlabeled data, relying on supervised learning alone is not sufficient for data-driven decision making in CPSs. Therefore, if we are to maximize the use of ML in CPSs, it is necessary to have explainable unsupervised ML models. In this paper, we outline how unsupervised explainable ML could be used within CPSs. We review the existing work in unsupervised ML, present initial desiderata of explainable unsupervised ML for CPS, and present a Self-Organizing Maps based explainable clustering methodology which generates global and local explanations. We evaluate the fidelity of the generated explanations using feature perturbation techniques. The results show that the proposed method identifies the most important features responsible for the decision-making process of Self-organizing Maps. Further, we demonstrated that explainable Self-Organizing Maps are a strong candidate for explainable unsupervised machine learning by comparing its model capabilities and limitations with current explainable unsupervised methods.https://ieeexplore.ieee.org/document/9536751/Explainable artificial intelligenceself-organizing mapsinterpretable machine learningunsupervised machine learning |
| spellingShingle | Chathurika S Wickramasinghe Kasun Amarasinghe Daniel L. Marino Craig Rieger Milos Manic Explainable Unsupervised Machine Learning for Cyber-Physical Systems IEEE Access Explainable artificial intelligence self-organizing maps interpretable machine learning unsupervised machine learning |
| title | Explainable Unsupervised Machine Learning for Cyber-Physical Systems |
| title_full | Explainable Unsupervised Machine Learning for Cyber-Physical Systems |
| title_fullStr | Explainable Unsupervised Machine Learning for Cyber-Physical Systems |
| title_full_unstemmed | Explainable Unsupervised Machine Learning for Cyber-Physical Systems |
| title_short | Explainable Unsupervised Machine Learning for Cyber-Physical Systems |
| title_sort | explainable unsupervised machine learning for cyber physical systems |
| topic | Explainable artificial intelligence self-organizing maps interpretable machine learning unsupervised machine learning |
| url | https://ieeexplore.ieee.org/document/9536751/ |
| work_keys_str_mv | AT chathurikaswickramasinghe explainableunsupervisedmachinelearningforcyberphysicalsystems AT kasunamarasinghe explainableunsupervisedmachinelearningforcyberphysicalsystems AT daniellmarino explainableunsupervisedmachinelearningforcyberphysicalsystems AT craigrieger explainableunsupervisedmachinelearningforcyberphysicalsystems AT milosmanic explainableunsupervisedmachinelearningforcyberphysicalsystems |