Adversarial attack detection framework based on optimized weighted conditional stepwise adversarial network Article Swipe
Kousik Barik
,
Sanjay Misra
,
Luis Fernández Sanz
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10207-024-00844-w
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10207-024-00844-w
Artificial Intelligence (AI)-based IDS systems are susceptible to adversarial attacks and face challenges such as complex evaluation methods, elevated false positive rates, absence of effective validation, and time-intensive processes. This study proposes a WCSAN-PSO framework to detect adversarial attacks in IDS based on a weighted conditional stepwise adversarial network (WCSAN) with a particle swarm optimization (PSO) algorithm and SVC (support vector classifier) for classification. The Principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) are used for feature selection and extraction. The PSO algorithm optimizes the parameters of the generator and discriminator in WCSAN to improve the adversarial training of IDS. The study presented three distinct scenarios with quantitative evaluation, and the proposed framework is evaluated with adversarial training in balanced and imbalanced data. Compared with existing studies, the proposed framework accomplished an accuracy of 99.36% in normal and 98.55% in malicious traffic in adversarial attacks. This study presents a comprehensive overview for researchers interested in adversarial attacks and their significance in computer security.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10207-024-00844-w
- https://link.springer.com/content/pdf/10.1007/s10207-024-00844-w.pdf
- OA Status
- hybrid
- Cited By
- 19
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394755835
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394755835Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10207-024-00844-wDigital Object Identifier
- Title
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Adversarial attack detection framework based on optimized weighted conditional stepwise adversarial networkWork 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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2024-04-12Full publication date if available
- Authors
-
Kousik Barik, Sanjay Misra, Luis Fernández SanzList of authors in order
- Landing page
-
https://doi.org/10.1007/s10207-024-00844-wPublisher landing page
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https://link.springer.com/content/pdf/10.1007/s10207-024-00844-w.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10207-024-00844-w.pdfDirect OA link when available
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Adversarial system, Computer science, Discriminator, Artificial intelligence, Feature selection, Classifier (UML), Data mining, Machine learning, Pattern recognition (psychology), Detector, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 13, 2024: 6Per-year citation counts (last 5 years)
- References (count)
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60Number 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.(WCSAN) | 50 |
| abstract_inverted_index.absence | 23 |
| abstract_inverted_index.attacks | 10, 39, 162 |
| abstract_inverted_index.complex | 16 |
| abstract_inverted_index.feature | 82 |
| abstract_inverted_index.improve | 100 |
| abstract_inverted_index.network | 49 |
| abstract_inverted_index.systems | 5 |
| abstract_inverted_index.traffic | 147 |
| abstract_inverted_index.(support | 60 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Compared | 129 |
| abstract_inverted_index.absolute | 73 |
| abstract_inverted_index.accuracy | 138 |
| abstract_inverted_index.analysis | 68 |
| abstract_inverted_index.attacks. | 150 |
| abstract_inverted_index.balanced | 125 |
| abstract_inverted_index.computer | 167 |
| abstract_inverted_index.distinct | 110 |
| abstract_inverted_index.elevated | 19 |
| abstract_inverted_index.existing | 131 |
| abstract_inverted_index.methods, | 18 |
| abstract_inverted_index.operator | 77 |
| abstract_inverted_index.overview | 156 |
| abstract_inverted_index.particle | 53 |
| abstract_inverted_index.positive | 21 |
| abstract_inverted_index.presents | 153 |
| abstract_inverted_index.proposed | 117, 134 |
| abstract_inverted_index.proposes | 32 |
| abstract_inverted_index.stepwise | 47 |
| abstract_inverted_index.studies, | 132 |
| abstract_inverted_index.training | 103, 123 |
| abstract_inverted_index.weighted | 45 |
| abstract_inverted_index.Principal | 66 |
| abstract_inverted_index.WCSAN-PSO | 34 |
| abstract_inverted_index.algorithm | 57, 88 |
| abstract_inverted_index.component | 67 |
| abstract_inverted_index.effective | 25 |
| abstract_inverted_index.evaluated | 120 |
| abstract_inverted_index.framework | 35, 118, 135 |
| abstract_inverted_index.generator | 94 |
| abstract_inverted_index.malicious | 146 |
| abstract_inverted_index.optimizes | 89 |
| abstract_inverted_index.presented | 108 |
| abstract_inverted_index.scenarios | 111 |
| abstract_inverted_index.security. | 168 |
| abstract_inverted_index.selection | 76, 83 |
| abstract_inverted_index.shrinkage | 74 |
| abstract_inverted_index.(AI)-based | 3 |
| abstract_inverted_index.Artificial | 1 |
| abstract_inverted_index.challenges | 13 |
| abstract_inverted_index.evaluation | 17 |
| abstract_inverted_index.imbalanced | 127 |
| abstract_inverted_index.interested | 159 |
| abstract_inverted_index.parameters | 91 |
| abstract_inverted_index.processes. | 29 |
| abstract_inverted_index.adversarial | 9, 38, 48, 102, 122, 149, 161 |
| abstract_inverted_index.classifier) | 62 |
| abstract_inverted_index.conditional | 46 |
| abstract_inverted_index.evaluation, | 114 |
| abstract_inverted_index.extraction. | 85 |
| abstract_inverted_index.researchers | 158 |
| abstract_inverted_index.susceptible | 7 |
| abstract_inverted_index.validation, | 26 |
| abstract_inverted_index.Intelligence | 2 |
| abstract_inverted_index.accomplished | 136 |
| abstract_inverted_index.optimization | 55 |
| abstract_inverted_index.quantitative | 113 |
| abstract_inverted_index.significance | 165 |
| abstract_inverted_index.comprehensive | 155 |
| abstract_inverted_index.discriminator | 96 |
| abstract_inverted_index.time-intensive | 28 |
| abstract_inverted_index.classification. | 64 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5064136287 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I3130438513 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6899999976158142 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.98283907 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |