Peer Review #1 of "Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning (v0.1)" Article Swipe
Seungjin Lee
,
Azween Bin
,
Noor Zaman
,
Hoong Kok
,
Azween Abdullah
,
Sim Kok
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.7287/peerj-cs.350v0.1/reviews/1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.7287/peerj-cs.350v0.1/reviews/1
Industrial revolution 4.0 with the breakthrough technological advances in 5G, and artificial intelligence has innovatively transformed the manufacturing industry from digitalization and automation to the new era of smart factories.A smart factory can do not only more than just produce products in a digital and automatic system, but also is able to optimize the production on its own by integrating production with process management, service distribution, and customized product requirement.A big challenge to the smart factory is to ensure that its network security can counteract with any cyber attacks such as botnet and DDos (Distributed Denial of Service), They are recognized to cause serious interruption in production, and consequently economic losses for company producers.Among many security solutions, botnet detection using honeypot has shown to be effective in some investigation studies.It is a method of detecting botnet attackers by intentionally creating a resource within the network with the purpose of closely monitoring and acquiring botnet attacking behaviors.For the first time, a proposed model of botnet detection was experimented by combing honeypot with machine learning to classify botnet attacks.A mimicking smart factory environment was created on IoT device hardware configuration.Experimental results showed that the model performance gave a high accuracy of above 96%, with very fast time taken of just 0.1 milliseconds and false positive rate at 0.24127 using random forest algorithm with Weka machine learning program.Hence, the honeypot combined machine learning model in this study was proved to be highly feasible to apply in the security network of smart factory to detect botnet attacks.
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Metadata
- Type
- peer-review
- Language
- en
- Landing Page
- https://doi.org/10.7287/peerj-cs.350v0.1/reviews/1
- OA Status
- gold
- References
- 29
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- OpenAlex ID
- https://openalex.org/W4235720728
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4235720728Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7287/peerj-cs.350v0.1/reviews/1Digital Object Identifier
- Title
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Peer Review #1 of "Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning (v0.1)"Work title
- Type
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peer-reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-25Full publication date if available
- Authors
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Seungjin Lee, Azween Bin, Noor Zaman, Hoong Kok, Azween Abdullah, Sim KokList of authors in order
- Landing page
-
https://doi.org/10.7287/peerj-cs.350v0.1/reviews/1Publisher landing page
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://doi.org/10.7287/peerj-cs.350v0.1/reviews/1Direct OA link when available
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Honeypot, Botnet, Computer science, Factory (object-oriented programming), Internet of Things, Computer security, Operating system, Embedded system, The Internet, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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29Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.fast | 203 |
| abstract_inverted_index.from | 19 |
| abstract_inverted_index.gave | 194 |
| abstract_inverted_index.high | 196 |
| abstract_inverted_index.just | 38, 207 |
| abstract_inverted_index.many | 114 |
| abstract_inverted_index.more | 36 |
| abstract_inverted_index.only | 35 |
| abstract_inverted_index.rate | 213 |
| abstract_inverted_index.some | 127 |
| abstract_inverted_index.such | 89 |
| abstract_inverted_index.than | 37 |
| abstract_inverted_index.that | 79, 190 |
| abstract_inverted_index.this | 232 |
| abstract_inverted_index.time | 204 |
| abstract_inverted_index.very | 202 |
| abstract_inverted_index.with | 3, 61, 85, 145, 170, 201, 220 |
| abstract_inverted_index.above | 199 |
| abstract_inverted_index.apply | 241 |
| abstract_inverted_index.cause | 102 |
| abstract_inverted_index.cyber | 87 |
| abstract_inverted_index.false | 211 |
| abstract_inverted_index.first | 157 |
| abstract_inverted_index.model | 161, 192, 230 |
| abstract_inverted_index.shown | 122 |
| abstract_inverted_index.smart | 28, 30, 74, 178, 247 |
| abstract_inverted_index.study | 233 |
| abstract_inverted_index.taken | 205 |
| abstract_inverted_index.time, | 158 |
| abstract_inverted_index.using | 119, 216 |
| abstract_inverted_index.Denial | 95 |
| abstract_inverted_index.botnet | 91, 117, 135, 153, 163, 175, 251 |
| abstract_inverted_index.detect | 250 |
| abstract_inverted_index.device | 185 |
| abstract_inverted_index.ensure | 78 |
| abstract_inverted_index.forest | 218 |
| abstract_inverted_index.highly | 238 |
| abstract_inverted_index.losses | 110 |
| abstract_inverted_index.method | 132 |
| abstract_inverted_index.proved | 235 |
| abstract_inverted_index.random | 217 |
| abstract_inverted_index.showed | 189 |
| abstract_inverted_index.within | 142 |
| abstract_inverted_index.0.24127 | 215 |
| abstract_inverted_index.attacks | 88 |
| abstract_inverted_index.closely | 149 |
| abstract_inverted_index.combing | 168 |
| abstract_inverted_index.company | 112 |
| abstract_inverted_index.created | 182 |
| abstract_inverted_index.digital | 43 |
| abstract_inverted_index.factory | 31, 75, 179, 248 |
| abstract_inverted_index.machine | 171, 222, 228 |
| abstract_inverted_index.network | 81, 144, 245 |
| abstract_inverted_index.process | 62 |
| abstract_inverted_index.produce | 39 |
| abstract_inverted_index.product | 68 |
| abstract_inverted_index.purpose | 147 |
| abstract_inverted_index.results | 188 |
| abstract_inverted_index.serious | 103 |
| abstract_inverted_index.service | 64 |
| abstract_inverted_index.system, | 46 |
| abstract_inverted_index.accuracy | 197 |
| abstract_inverted_index.advances | 7 |
| abstract_inverted_index.attacks. | 252 |
| abstract_inverted_index.classify | 174 |
| abstract_inverted_index.combined | 227 |
| abstract_inverted_index.creating | 139 |
| abstract_inverted_index.economic | 109 |
| abstract_inverted_index.feasible | 239 |
| abstract_inverted_index.hardware | 186 |
| abstract_inverted_index.honeypot | 120, 169, 226 |
| abstract_inverted_index.industry | 18 |
| abstract_inverted_index.learning | 172, 223, 229 |
| abstract_inverted_index.optimize | 52 |
| abstract_inverted_index.positive | 212 |
| abstract_inverted_index.products | 40 |
| abstract_inverted_index.proposed | 160 |
| abstract_inverted_index.resource | 141 |
| abstract_inverted_index.security | 82, 115, 244 |
| abstract_inverted_index.Service), | 97 |
| abstract_inverted_index.acquiring | 152 |
| abstract_inverted_index.algorithm | 219 |
| abstract_inverted_index.attackers | 136 |
| abstract_inverted_index.attacking | 154 |
| abstract_inverted_index.attacks.A | 176 |
| abstract_inverted_index.automatic | 45 |
| abstract_inverted_index.challenge | 71 |
| abstract_inverted_index.detecting | 134 |
| abstract_inverted_index.detection | 118, 164 |
| abstract_inverted_index.effective | 125 |
| abstract_inverted_index.mimicking | 177 |
| abstract_inverted_index.Industrial | 0 |
| abstract_inverted_index.artificial | 11 |
| abstract_inverted_index.automation | 22 |
| abstract_inverted_index.counteract | 84 |
| abstract_inverted_index.customized | 67 |
| abstract_inverted_index.monitoring | 150 |
| abstract_inverted_index.production | 54, 60 |
| abstract_inverted_index.recognized | 100 |
| abstract_inverted_index.revolution | 1 |
| abstract_inverted_index.solutions, | 116 |
| abstract_inverted_index.studies.It | 129 |
| abstract_inverted_index.environment | 180 |
| abstract_inverted_index.factories.A | 29 |
| abstract_inverted_index.integrating | 59 |
| abstract_inverted_index.management, | 63 |
| abstract_inverted_index.performance | 193 |
| abstract_inverted_index.production, | 106 |
| abstract_inverted_index.transformed | 15 |
| abstract_inverted_index.(Distributed | 94 |
| abstract_inverted_index.breakthrough | 5 |
| abstract_inverted_index.consequently | 108 |
| abstract_inverted_index.experimented | 166 |
| abstract_inverted_index.innovatively | 14 |
| abstract_inverted_index.intelligence | 12 |
| abstract_inverted_index.interruption | 104 |
| abstract_inverted_index.milliseconds | 209 |
| abstract_inverted_index.behaviors.For | 155 |
| abstract_inverted_index.distribution, | 65 |
| abstract_inverted_index.intentionally | 138 |
| abstract_inverted_index.investigation | 128 |
| abstract_inverted_index.manufacturing | 17 |
| abstract_inverted_index.requirement.A | 69 |
| abstract_inverted_index.technological | 6 |
| abstract_inverted_index.digitalization | 20 |
| abstract_inverted_index.program.Hence, | 224 |
| abstract_inverted_index.producers.Among | 113 |
| abstract_inverted_index.configuration.Experimental | 187 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100734808 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I4210143550 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |