Particle Swarm Optimization Feature Extraction Technique for Intrusion Detection System Article Swipe
Vaishnavi Ganesh
,
Manmohan Sharma
,
Santosh Kumar Henge
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2412032/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2412032/v1
The task of ensuring cyber-security has grown increasingly challenging as the alarming expansion of computer connectivity and the large number of computer-related applications has expanded recently. It also requires a sufficient protection system against a variety of cyberattacks. Detecting discrepancies and risks in a computer network, as well as creating intrusion detection systems (IDS) to aid in cyber-security. Artificial intelligence (AI), specifically machine learning (ML) approaches, were used to create a practical data-driven intrusion detection system. Two alternative intrusion detection (ID) classification approaches were compared in this study, each with its own set of use cases. Before using the two classifiers for classification, the Particle Swarm Optimization (PSO) approach was used to reduce dimensionality. The classification approaches used to characterise network anomalies were studied in this study. PSO + ANN (Artificial neural network), PSO + Decision Tree (PSO+DT) and PSO + K-Nearest Neighbor (PSO+KNN) are the three classifiers used. The detection approaches' results were confirmed using the KDD-CUP 99 dataset. On the result of the implementation, success indicators like as specificity, recall, f1-score, accuracy, precision, and consistency were used on cyber-security databases for different types of cyber-attacks. The accuracy, detection rate (DR), and false-positive rate of the two classifiers were also compared to see which one outperforms the other (FPR). Finally, the system was compared to the IDS that was already in place. In terms of detecting network anomalies, the results reveal that PSO+ANN outperforms the PSO+KNN and PSO+DT classifier algorithms.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2412032/v1
- https://www.researchsquare.com/article/rs-2412032/latest.pdf
- OA Status
- green
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313491785
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313491785Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-2412032/v1Digital Object Identifier
- Title
-
Particle Swarm Optimization Feature Extraction Technique for Intrusion Detection SystemWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-04Full publication date if available
- Authors
-
Vaishnavi Ganesh, Manmohan Sharma, Santosh Kumar HengeList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2412032/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-2412032/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-2412032/latest.pdfDirect OA link when available
- Concepts
-
Intrusion detection system, Particle swarm optimization, Computer science, Artificial intelligence, Machine learning, Artificial neural network, Classifier (UML), Data mining, Curse of dimensionality, Network security, Pattern recognition (psychology), Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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50Number 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.other | 209 |
| abstract_inverted_index.risks | 42 |
| abstract_inverted_index.terms | 225 |
| abstract_inverted_index.three | 147 |
| abstract_inverted_index.types | 185 |
| abstract_inverted_index.used. | 149 |
| abstract_inverted_index.using | 98, 156 |
| abstract_inverted_index.which | 205 |
| abstract_inverted_index.(FPR). | 210 |
| abstract_inverted_index.Before | 97 |
| abstract_inverted_index.PSO+DT | 239 |
| abstract_inverted_index.cases. | 96 |
| abstract_inverted_index.create | 70 |
| abstract_inverted_index.neural | 132 |
| abstract_inverted_index.number | 20 |
| abstract_inverted_index.place. | 223 |
| abstract_inverted_index.reduce | 113 |
| abstract_inverted_index.result | 163 |
| abstract_inverted_index.reveal | 232 |
| abstract_inverted_index.study, | 88 |
| abstract_inverted_index.study. | 127 |
| abstract_inverted_index.system | 33, 213 |
| abstract_inverted_index.KDD-CUP | 158 |
| abstract_inverted_index.PSO+ANN | 234 |
| abstract_inverted_index.PSO+KNN | 237 |
| abstract_inverted_index.against | 34 |
| abstract_inverted_index.already | 221 |
| abstract_inverted_index.machine | 63 |
| abstract_inverted_index.network | 121, 228 |
| abstract_inverted_index.recall, | 172 |
| abstract_inverted_index.results | 153, 231 |
| abstract_inverted_index.studied | 124 |
| abstract_inverted_index.success | 167 |
| abstract_inverted_index.system. | 76 |
| abstract_inverted_index.systems | 53 |
| abstract_inverted_index.variety | 36 |
| abstract_inverted_index.(PSO+DT) | 138 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Decision | 136 |
| abstract_inverted_index.Finally, | 211 |
| abstract_inverted_index.Neighbor | 143 |
| abstract_inverted_index.Particle | 105 |
| abstract_inverted_index.alarming | 12 |
| abstract_inverted_index.approach | 109 |
| abstract_inverted_index.compared | 85, 202, 215 |
| abstract_inverted_index.computer | 15, 45 |
| abstract_inverted_index.creating | 50 |
| abstract_inverted_index.dataset. | 160 |
| abstract_inverted_index.ensuring | 4 |
| abstract_inverted_index.expanded | 25 |
| abstract_inverted_index.learning | 64 |
| abstract_inverted_index.network, | 46 |
| abstract_inverted_index.requires | 29 |
| abstract_inverted_index.(PSO+KNN) | 144 |
| abstract_inverted_index.Detecting | 39 |
| abstract_inverted_index.K-Nearest | 142 |
| abstract_inverted_index.accuracy, | 174, 189 |
| abstract_inverted_index.anomalies | 122 |
| abstract_inverted_index.confirmed | 155 |
| abstract_inverted_index.databases | 182 |
| abstract_inverted_index.detecting | 227 |
| abstract_inverted_index.detection | 52, 75, 80, 151, 190 |
| abstract_inverted_index.different | 184 |
| abstract_inverted_index.expansion | 13 |
| abstract_inverted_index.f1-score, | 173 |
| abstract_inverted_index.intrusion | 51, 74, 79 |
| abstract_inverted_index.network), | 133 |
| abstract_inverted_index.practical | 72 |
| abstract_inverted_index.recently. | 26 |
| abstract_inverted_index.Artificial | 59 |
| abstract_inverted_index.anomalies, | 229 |
| abstract_inverted_index.approaches | 83, 117 |
| abstract_inverted_index.classifier | 240 |
| abstract_inverted_index.indicators | 168 |
| abstract_inverted_index.precision, | 175 |
| abstract_inverted_index.protection | 32 |
| abstract_inverted_index.sufficient | 31 |
| abstract_inverted_index.(Artificial | 131 |
| abstract_inverted_index.algorithms. | 241 |
| abstract_inverted_index.alternative | 78 |
| abstract_inverted_index.approaches' | 152 |
| abstract_inverted_index.approaches, | 66 |
| abstract_inverted_index.challenging | 9 |
| abstract_inverted_index.classifiers | 101, 148, 199 |
| abstract_inverted_index.consistency | 177 |
| abstract_inverted_index.data-driven | 73 |
| abstract_inverted_index.outperforms | 207, 235 |
| abstract_inverted_index.Optimization | 107 |
| abstract_inverted_index.applications | 23 |
| abstract_inverted_index.characterise | 120 |
| abstract_inverted_index.connectivity | 16 |
| abstract_inverted_index.increasingly | 8 |
| abstract_inverted_index.intelligence | 60 |
| abstract_inverted_index.specifically | 62 |
| abstract_inverted_index.specificity, | 171 |
| abstract_inverted_index.cyberattacks. | 38 |
| abstract_inverted_index.discrepancies | 40 |
| abstract_inverted_index.classification | 82, 116 |
| abstract_inverted_index.cyber-attacks. | 187 |
| abstract_inverted_index.cyber-security | 5, 181 |
| abstract_inverted_index.false-positive | 194 |
| abstract_inverted_index.classification, | 103 |
| abstract_inverted_index.cyber-security. | 58 |
| abstract_inverted_index.dimensionality. | 114 |
| abstract_inverted_index.implementation, | 166 |
| abstract_inverted_index.computer-related | 22 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5109597905 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I110360157 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6899999976158142 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.00421904 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |