AICrit: A Design-Enhanced Anomaly Detector and Its Performance Assessment in a Water Treatment Plant Article Swipe
Gauthama Raman
,
Aditya P. Mathur
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app132413124
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app132413124
Critical Infrastructure Security Showdown 2021—Online (CISS2021-OL) represented the fifth run of iTrust’s international technology assessment exercise. During this event, researchers and experts from the industry evaluated the performance of technologies designed to detect and mitigate real-time cyber-physical attacks launched against the operational iTrust testbeds and digital twins. Here, we summarize the performance of an anomaly detection mechanism, named AICrit, that was used during the exercise. AICrit utilizes the plant’s design to determine the models to be created using machine learning, and hence is referred to as a “design-enhanced” anomaly detector. The results of the validation in this large-scale exercise reveal that AICrit successfully detected 95.83% of the 27 launched attacks. Our analysis offers valuable insights into AICrit’s efficiency in detecting process anomalies in a water treatment plant under a continuous barrage of cyber-physical attacks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app132413124
- https://www.mdpi.com/2076-3417/13/24/13124/pdf?version=1702260215
- OA Status
- gold
- Cited By
- 2
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389571604
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4389571604Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/app132413124Digital Object Identifier
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AICrit: A Design-Enhanced Anomaly Detector and Its Performance Assessment in a Water Treatment PlantWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-09Full publication date if available
- Authors
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Gauthama Raman, Aditya P. MathurList of authors in order
- Landing page
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https://doi.org/10.3390/app132413124Publisher landing page
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https://www.mdpi.com/2076-3417/13/24/13124/pdf?version=1702260215Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/13/24/13124/pdf?version=1702260215Direct OA link when available
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Anomaly detection, Computer science, Anomaly (physics), Event (particle physics), Launched, Engineering, Data mining, Electrical engineering, Physics, Condensed matter physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.iTrust’s | 11 |
| abstract_inverted_index.mechanism, | 56 |
| abstract_inverted_index.technology | 13 |
| abstract_inverted_index.validation | 94 |
| abstract_inverted_index.large-scale | 97 |
| abstract_inverted_index.operational | 41 |
| abstract_inverted_index.performance | 27, 51 |
| abstract_inverted_index.represented | 6 |
| abstract_inverted_index.researchers | 19 |
| abstract_inverted_index.successfully | 102 |
| abstract_inverted_index.technologies | 29 |
| abstract_inverted_index.(CISS2021-OL) | 5 |
| abstract_inverted_index.2021—Online | 4 |
| abstract_inverted_index.international | 12 |
| abstract_inverted_index.Infrastructure | 1 |
| abstract_inverted_index.cyber-physical | 36, 132 |
| abstract_inverted_index.“design-enhanced” | 87 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5102685293 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I152815399 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.65013148 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |