AD-NEv: A Scalable Multilevel Neuroevolution Framework for Multivariate Anomaly Detection Article Swipe
Marcin Pietroń
,
Dominik Żurek
,
Kamil Faber
,
Roberto Corizzo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tnnls.2024.3439404
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tnnls.2024.3439404
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time-consuming process. Neuroevolution could be an effective and efficient solution to this problem, as a fully automated search method for learning optimal neural networks, supporting both gradient and nongradient fine-tuning. However, existing methods mostly focus on optimizing model architectures without taking into account feature subspaces and model weights. In this work, we propose anomaly detection neuroevolution (AD-NEv)-a scalable multilevel optimized neuroevolution framework for multivariate time-series anomaly detection. The method represents a novel approach to synergically: 1) optimize feature subspaces for an ensemble model based on the bagging technique; 2) optimize the model architecture of single anomaly detection models; and 3) perform nongradient fine-tuning of network weights. An extensive experimental evaluation on widely adopted multivariate anomaly detection benchmark datasets shows that the models extracted by AD-NEv outperform well-known deep learning architectures for anomaly detection. Moreover, results show that AD-NEv can perform the whole process efficiently, presenting high scalability when multiple graphics processing units (GPUs) are available.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnnls.2024.3439404
- OA Status
- hybrid
- Cited By
- 4
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4401596698Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tnnls.2024.3439404Digital Object Identifier
- Title
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AD-NEv: A Scalable Multilevel Neuroevolution Framework for Multivariate Anomaly DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-14Full publication date if available
- Authors
-
Marcin Pietroń, Dominik Żurek, Kamil Faber, Roberto CorizzoList of authors in order
- Landing page
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https://doi.org/10.1109/tnnls.2024.3439404Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1109/tnnls.2024.3439404Direct OA link when available
- Concepts
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Multivariate statistics, Anomaly detection, Computer science, Neuroevolution, Anomaly (physics), Scalability, Univariate, Artificial intelligence, Artificial neural network, Machine learning, Physics, Condensed matter physics, DatabaseTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3040266635, https://openalex.org/W6720514713, https://openalex.org/W2965433388, https://openalex.org/W2977929929, https://openalex.org/W2950361482, https://openalex.org/W3081497074, https://openalex.org/W2950757722, https://openalex.org/W6745040976, https://openalex.org/W2963523189, https://openalex.org/W4313590336, https://openalex.org/W4378530079, https://openalex.org/W3205303036, https://openalex.org/W3155756722, https://openalex.org/W3195516695, https://openalex.org/W2963166639, https://openalex.org/W4389403158, https://openalex.org/W3169450514, https://openalex.org/W4388676625, https://openalex.org/W4392607667, https://openalex.org/W2786827964, https://openalex.org/W3198059351, https://openalex.org/W4389538721, https://openalex.org/W2111935653, https://openalex.org/W2119814172, https://openalex.org/W6734864916, https://openalex.org/W3211518947, https://openalex.org/W2122646361, https://openalex.org/W6758101687, https://openalex.org/W3198381997, https://openalex.org/W4376288669, https://openalex.org/W3184778778, https://openalex.org/W3155567600, https://openalex.org/W3152030785, https://openalex.org/W6749825310, https://openalex.org/W2962736999, https://openalex.org/W6748102297, https://openalex.org/W3161570989, https://openalex.org/W2344848374, https://openalex.org/W2886869472, https://openalex.org/W4285038805, https://openalex.org/W6796977264, https://openalex.org/W3189447831, https://openalex.org/W3135737072, https://openalex.org/W6776767859, https://openalex.org/W6780248173, https://openalex.org/W2785362611, https://openalex.org/W6680067488, https://openalex.org/W2171180835, https://openalex.org/W2407991977, https://openalex.org/W2604247107, https://openalex.org/W3199473923 |
| referenced_works_count | 51 |
| abstract_inverted_index.a | 6, 30, 34, 51, 107 |
| abstract_inverted_index.1) | 112 |
| abstract_inverted_index.2) | 125 |
| abstract_inverted_index.3) | 136 |
| abstract_inverted_index.An | 143 |
| abstract_inverted_index.In | 85 |
| abstract_inverted_index.an | 42, 117 |
| abstract_inverted_index.as | 50 |
| abstract_inverted_index.be | 41 |
| abstract_inverted_index.by | 160 |
| abstract_inverted_index.in | 9, 20 |
| abstract_inverted_index.is | 33 |
| abstract_inverted_index.of | 130, 140 |
| abstract_inverted_index.on | 72, 121, 147 |
| abstract_inverted_index.to | 47, 110 |
| abstract_inverted_index.we | 88 |
| abstract_inverted_index.The | 104 |
| abstract_inverted_index.and | 3, 12, 36, 44, 64, 82, 135 |
| abstract_inverted_index.are | 190 |
| abstract_inverted_index.can | 175 |
| abstract_inverted_index.for | 24, 29, 56, 99, 116, 167 |
| abstract_inverted_index.key | 7 |
| abstract_inverted_index.the | 17, 122, 127, 157, 177 |
| abstract_inverted_index.both | 62 |
| abstract_inverted_index.deep | 21, 164 |
| abstract_inverted_index.high | 182 |
| abstract_inverted_index.into | 78 |
| abstract_inverted_index.show | 172 |
| abstract_inverted_index.that | 156, 173 |
| abstract_inverted_index.this | 48, 86 |
| abstract_inverted_index.when | 184 |
| abstract_inverted_index.based | 120 |
| abstract_inverted_index.could | 40 |
| abstract_inverted_index.focus | 71 |
| abstract_inverted_index.fully | 52 |
| abstract_inverted_index.given | 31 |
| abstract_inverted_index.model | 27, 74, 83, 119, 128 |
| abstract_inverted_index.novel | 108 |
| abstract_inverted_index.shows | 155 |
| abstract_inverted_index.tools | 2 |
| abstract_inverted_index.units | 188 |
| abstract_inverted_index.whole | 178 |
| abstract_inverted_index.work, | 87 |
| abstract_inverted_index.(GPUs) | 189 |
| abstract_inverted_index.AD-NEv | 161, 174 |
| abstract_inverted_index.method | 55, 105 |
| abstract_inverted_index.models | 158 |
| abstract_inverted_index.modern | 10 |
| abstract_inverted_index.mostly | 70 |
| abstract_inverted_index.neural | 59 |
| abstract_inverted_index.search | 54 |
| abstract_inverted_index.single | 131 |
| abstract_inverted_index.taking | 77 |
| abstract_inverted_index.widely | 148 |
| abstract_inverted_index.Anomaly | 0 |
| abstract_inverted_index.Despite | 16 |
| abstract_inverted_index.account | 79 |
| abstract_inverted_index.adopted | 149 |
| abstract_inverted_index.anomaly | 25, 90, 102, 132, 151, 168 |
| abstract_inverted_index.bagging | 123 |
| abstract_inverted_index.dataset | 32 |
| abstract_inverted_index.failure | 13 |
| abstract_inverted_index.feature | 80, 114 |
| abstract_inverted_index.methods | 4, 69 |
| abstract_inverted_index.models; | 134 |
| abstract_inverted_index.network | 141 |
| abstract_inverted_index.optimal | 58 |
| abstract_inverted_index.perform | 137, 176 |
| abstract_inverted_index.present | 5 |
| abstract_inverted_index.process | 179 |
| abstract_inverted_index.propose | 89 |
| abstract_inverted_index.results | 171 |
| abstract_inverted_index.without | 76 |
| abstract_inverted_index.However, | 67 |
| abstract_inverted_index.approach | 109 |
| abstract_inverted_index.datasets | 154 |
| abstract_inverted_index.ensemble | 118 |
| abstract_inverted_index.existing | 68 |
| abstract_inverted_index.gradient | 63 |
| abstract_inverted_index.graphics | 186 |
| abstract_inverted_index.learning | 22, 57, 165 |
| abstract_inverted_index.multiple | 185 |
| abstract_inverted_index.optimize | 113, 126 |
| abstract_inverted_index.problem, | 49 |
| abstract_inverted_index.process. | 38 |
| abstract_inverted_index.scalable | 94 |
| abstract_inverted_index.solution | 46 |
| abstract_inverted_index.systems. | 15 |
| abstract_inverted_index.weights. | 84, 142 |
| abstract_inverted_index.Moreover, | 170 |
| abstract_inverted_index.automated | 53 |
| abstract_inverted_index.benchmark | 153 |
| abstract_inverted_index.detection | 1, 91, 133, 152 |
| abstract_inverted_index.effective | 43 |
| abstract_inverted_index.efficient | 45 |
| abstract_inverted_index.extensive | 144 |
| abstract_inverted_index.extracted | 159 |
| abstract_inverted_index.framework | 98 |
| abstract_inverted_index.networks, | 60 |
| abstract_inverted_index.optimized | 96 |
| abstract_inverted_index.subspaces | 81, 115 |
| abstract_inverted_index.(AD-NEv)-a | 93 |
| abstract_inverted_index.available. | 191 |
| abstract_inverted_index.capability | 8 |
| abstract_inverted_index.cumbersome | 35 |
| abstract_inverted_index.detection, | 26 |
| abstract_inverted_index.detection. | 103, 169 |
| abstract_inverted_index.evaluation | 146 |
| abstract_inverted_index.fast-paced | 18 |
| abstract_inverted_index.multilevel | 95 |
| abstract_inverted_index.optimizing | 73 |
| abstract_inverted_index.outperform | 162 |
| abstract_inverted_index.prediction | 14 |
| abstract_inverted_index.presenting | 181 |
| abstract_inverted_index.processing | 187 |
| abstract_inverted_index.represents | 106 |
| abstract_inverted_index.supporting | 61 |
| abstract_inverted_index.technique; | 124 |
| abstract_inverted_index.well-known | 163 |
| abstract_inverted_index.development | 19 |
| abstract_inverted_index.fine-tuning | 139 |
| abstract_inverted_index.nongradient | 65, 138 |
| abstract_inverted_index.scalability | 183 |
| abstract_inverted_index.time-series | 101 |
| abstract_inverted_index.architecture | 129 |
| abstract_inverted_index.efficiently, | 180 |
| abstract_inverted_index.experimental | 145 |
| abstract_inverted_index.fine-tuning. | 66 |
| abstract_inverted_index.multivariate | 100, 150 |
| abstract_inverted_index.optimization | 28 |
| abstract_inverted_index.architectures | 23, 75, 166 |
| abstract_inverted_index.cyberphysical | 11 |
| abstract_inverted_index.synergically: | 111 |
| abstract_inverted_index.Neuroevolution | 39 |
| abstract_inverted_index.neuroevolution | 92, 97 |
| abstract_inverted_index.time-consuming | 37 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.87806592 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |