Detection of Random False Data Injection Cyberattacks in Smart Water Systems Using Optimized Deep Neural Networks Article Swipe
Faegheh Moazeni
,
Javad Khazaei
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/en15134832
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/en15134832
A cyberattack detection model based on supervised deep neural network is proposed to identify random false data injection (FDI) on the tank’s level measurements of a water distribution system. The architecture of the neural network, as well as various hyper-parameters, is modified and tuned to acquire the highest detection performance using the smallest size of training data set. The efficacy of the proposed detection model against various activation functions including sigmoid, rectified linear unit, and softmax is examined. Regularization and momentum techniques are applied to update the weights and prohibit overfitting. Moreover, statistical metrics are presented to evaluate the performance and effectiveness of the proposed model in the presence of a range of measurement noise levels. The proposed model is tested for three attack scenarios composed for the battle of the attack detection algorithms. Results confirm that the size of the data sets required to train the neural network (NN) to accomplish the highest levels of accuracy and precision is significantly decreased as the number of hidden layers is increased. The trained 4- and 5-layer deep neural networks are able to detect the readings’ FDIs with 100% precision and accuracy in the presence of 30% background noise in the sensory data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en15134832
- https://www.mdpi.com/1996-1073/15/13/4832/pdf?version=1656662799
- OA Status
- gold
- Cited By
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- References
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- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4283778239Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/en15134832Digital Object Identifier
- Title
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Detection of Random False Data Injection Cyberattacks in Smart Water Systems Using Optimized Deep Neural NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-07-01Full publication date if available
- Authors
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Faegheh Moazeni, Javad KhazaeiList of authors in order
- Landing page
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https://doi.org/10.3390/en15134832Publisher landing page
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https://www.mdpi.com/1996-1073/15/13/4832/pdf?version=1656662799Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1996-1073/15/13/4832/pdf?version=1656662799Direct OA link when available
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Softmax function, Overfitting, Computer science, Artificial neural network, Sigmoid function, Artificial intelligence, Noise (video), Data set, Pattern recognition (psychology), Random forest, Data mining, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 1, 2024: 4, 2023: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.size | 53, 138 |
| abstract_inverted_index.that | 136 |
| abstract_inverted_index.well | 36 |
| abstract_inverted_index.with | 185 |
| abstract_inverted_index.(FDI) | 18 |
| abstract_inverted_index.based | 4 |
| abstract_inverted_index.data. | 200 |
| abstract_inverted_index.false | 15 |
| abstract_inverted_index.level | 22 |
| abstract_inverted_index.model | 3, 64, 105, 118 |
| abstract_inverted_index.noise | 114, 196 |
| abstract_inverted_index.range | 111 |
| abstract_inverted_index.three | 122 |
| abstract_inverted_index.train | 145 |
| abstract_inverted_index.tuned | 43 |
| abstract_inverted_index.unit, | 73 |
| abstract_inverted_index.using | 50 |
| abstract_inverted_index.water | 26 |
| abstract_inverted_index.attack | 123, 131 |
| abstract_inverted_index.battle | 128 |
| abstract_inverted_index.detect | 181 |
| abstract_inverted_index.hidden | 166 |
| abstract_inverted_index.layers | 167 |
| abstract_inverted_index.levels | 154 |
| abstract_inverted_index.linear | 72 |
| abstract_inverted_index.neural | 8, 33, 147, 176 |
| abstract_inverted_index.number | 164 |
| abstract_inverted_index.random | 14 |
| abstract_inverted_index.tested | 120 |
| abstract_inverted_index.update | 85 |
| abstract_inverted_index.5-layer | 174 |
| abstract_inverted_index.Results | 134 |
| abstract_inverted_index.acquire | 45 |
| abstract_inverted_index.against | 65 |
| abstract_inverted_index.applied | 83 |
| abstract_inverted_index.confirm | 135 |
| abstract_inverted_index.highest | 47, 153 |
| abstract_inverted_index.levels. | 115 |
| abstract_inverted_index.metrics | 93 |
| abstract_inverted_index.network | 9, 148 |
| abstract_inverted_index.sensory | 199 |
| abstract_inverted_index.softmax | 75 |
| abstract_inverted_index.system. | 28 |
| abstract_inverted_index.trained | 171 |
| abstract_inverted_index.various | 38, 66 |
| abstract_inverted_index.weights | 87 |
| abstract_inverted_index.accuracy | 156, 189 |
| abstract_inverted_index.composed | 125 |
| abstract_inverted_index.efficacy | 59 |
| abstract_inverted_index.evaluate | 97 |
| abstract_inverted_index.identify | 13 |
| abstract_inverted_index.modified | 41 |
| abstract_inverted_index.momentum | 80 |
| abstract_inverted_index.network, | 34 |
| abstract_inverted_index.networks | 177 |
| abstract_inverted_index.presence | 108, 192 |
| abstract_inverted_index.prohibit | 89 |
| abstract_inverted_index.proposed | 11, 62, 104, 117 |
| abstract_inverted_index.required | 143 |
| abstract_inverted_index.sigmoid, | 70 |
| abstract_inverted_index.smallest | 52 |
| abstract_inverted_index.tank’s | 21 |
| abstract_inverted_index.training | 55 |
| abstract_inverted_index.Moreover, | 91 |
| abstract_inverted_index.decreased | 161 |
| abstract_inverted_index.detection | 2, 48, 63, 132 |
| abstract_inverted_index.examined. | 77 |
| abstract_inverted_index.functions | 68 |
| abstract_inverted_index.including | 69 |
| abstract_inverted_index.injection | 17 |
| abstract_inverted_index.precision | 158, 187 |
| abstract_inverted_index.presented | 95 |
| abstract_inverted_index.rectified | 71 |
| abstract_inverted_index.scenarios | 124 |
| abstract_inverted_index.accomplish | 151 |
| abstract_inverted_index.activation | 67 |
| abstract_inverted_index.background | 195 |
| abstract_inverted_index.increased. | 169 |
| abstract_inverted_index.supervised | 6 |
| abstract_inverted_index.techniques | 81 |
| abstract_inverted_index.algorithms. | 133 |
| abstract_inverted_index.cyberattack | 1 |
| abstract_inverted_index.measurement | 113 |
| abstract_inverted_index.performance | 49, 99 |
| abstract_inverted_index.readings’ | 183 |
| abstract_inverted_index.statistical | 92 |
| abstract_inverted_index.architecture | 30 |
| abstract_inverted_index.distribution | 27 |
| abstract_inverted_index.measurements | 23 |
| abstract_inverted_index.overfitting. | 90 |
| abstract_inverted_index.effectiveness | 101 |
| abstract_inverted_index.significantly | 160 |
| abstract_inverted_index.Regularization | 78 |
| abstract_inverted_index.hyper-parameters, | 39 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5007059001 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I186143895 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.79643029 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |