An Optimization Technique for Intrusion Detection of Industrial Control Network Vulnerabilities Based on BP Neural Network Article Swipe
Wenzhong Xia
,
Rahul Neware
,
Sanjeet Kumar
,
D.A. Karras
,
Ali Rizwan
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-990908/v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-990908/v1
The aim of this research is to solve the problem that the intrusion detection model of industrial control system has low detection rate and detection efficiency against various attacks, a method of optimizing BP neural network based on Adaboost algorithm is proposed. Firstly, principal component analysis (PCA) is used to preprocess the original data set to eliminate its correlation. Secondly, Adaboost algorithm is used to continuously adjust the weight of training samples, to obtain the optimal weight and threshold of BP neural network. The results show that there are 13817 pieces of data collected in the industrial control experiment, of which 9817 pieces of data are taken as the test data set, including 9770 pieces of normal data and 47 pieces of abnormal data. In addition, as a test data set of 4000 pieces, there are 3987 pieces of normal data and 13 pieces of abnormal data. It can be seen that the average detection rate and detection speed of the algorithm of optimizing BP neural network by Adaboost algorithm proposed in this paper are better than other algorithms on each attack type. It is proved that Adaboost algorithm can effectively solve the intrusion detection problem by optimizing BP neural network.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-990908/v1
- https://www.researchsquare.com/article/rs-990908/latest.pdf
- OA Status
- green
- Cited By
- 3
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200473488
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200473488Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-990908/v1Digital Object Identifier
- Title
-
An Optimization Technique for Intrusion Detection of Industrial Control Network Vulnerabilities Based on BP Neural NetworkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-30Full publication date if available
- Authors
-
Wenzhong Xia, Rahul Neware, Sanjeet Kumar, D.A. Karras, Ali RizwanList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-990908/v1Publisher landing page
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https://www.researchsquare.com/article/rs-990908/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-990908/latest.pdfDirect OA link when available
- Concepts
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Artificial neural network, Intrusion detection system, Computer science, Control (management), Intrusion, Data mining, Artificial intelligence, Geology, GeochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2023: 3Per-year citation counts (last 5 years)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.method | 31 |
| abstract_inverted_index.neural | 35, 82, 166, 200 |
| abstract_inverted_index.normal | 117, 140 |
| abstract_inverted_index.obtain | 74 |
| abstract_inverted_index.pieces | 91, 103, 115, 121, 138, 144 |
| abstract_inverted_index.proved | 186 |
| abstract_inverted_index.system | 19 |
| abstract_inverted_index.weight | 69, 77 |
| abstract_inverted_index.against | 27 |
| abstract_inverted_index.average | 154 |
| abstract_inverted_index.control | 18, 98 |
| abstract_inverted_index.network | 36, 167 |
| abstract_inverted_index.optimal | 76 |
| abstract_inverted_index.pieces, | 134 |
| abstract_inverted_index.problem | 10, 196 |
| abstract_inverted_index.results | 85 |
| abstract_inverted_index.various | 28 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Adaboost | 39, 61, 169, 188 |
| abstract_inverted_index.Firstly, | 43 |
| abstract_inverted_index.abnormal | 123, 146 |
| abstract_inverted_index.analysis | 46 |
| abstract_inverted_index.attacks, | 29 |
| abstract_inverted_index.network. | 83, 201 |
| abstract_inverted_index.original | 53 |
| abstract_inverted_index.proposed | 171 |
| abstract_inverted_index.research | 5 |
| abstract_inverted_index.samples, | 72 |
| abstract_inverted_index.training | 71 |
| abstract_inverted_index.Secondly, | 60 |
| abstract_inverted_index.addition, | 126 |
| abstract_inverted_index.algorithm | 40, 62, 162, 170, 189 |
| abstract_inverted_index.collected | 94 |
| abstract_inverted_index.component | 45 |
| abstract_inverted_index.detection | 14, 22, 25, 155, 158, 195 |
| abstract_inverted_index.eliminate | 57 |
| abstract_inverted_index.including | 113 |
| abstract_inverted_index.intrusion | 13, 194 |
| abstract_inverted_index.principal | 44 |
| abstract_inverted_index.proposed. | 42 |
| abstract_inverted_index.threshold | 79 |
| abstract_inverted_index.algorithms | 179 |
| abstract_inverted_index.efficiency | 26 |
| abstract_inverted_index.industrial | 17, 97 |
| abstract_inverted_index.optimizing | 33, 164, 198 |
| abstract_inverted_index.preprocess | 51 |
| abstract_inverted_index.effectively | 191 |
| abstract_inverted_index.experiment, | 99 |
| abstract_inverted_index.continuously | 66 |
| abstract_inverted_index.correlation. | 59 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 5 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.6775246 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |