Externally validating the IoTDevID device identification methodology using the CIC IoT 2022 Dataset Article Swipe
Kahraman Kostas
,
Mike Just
,
Michael A. Lones
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.08679
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.08679
In the era of rapid IoT device proliferation, recognizing, diagnosing, and securing these devices are crucial tasks. The IoTDevID method (IEEE Internet of Things 2022) proposes a machine learning approach for device identification using network packet features. In this article we present a validation study of the IoTDevID method by testing core components, namely its feature set and its aggregation algorithm, on a new dataset. The new dataset (CIC-IoT-2022) offers several advantages over earlier datasets, including a larger number of devices, multiple instances of the same device, both IP and non-IP device data, normal (benign) usage data, and diverse usage profiles, such as active and idle states. Using this independent dataset, we explore the validity of IoTDevID's core components, and also examine the impacts of the new data on model performance. Our results indicate that data diversity is important to model performance. For example, models trained with active usage data outperformed those trained with idle usage data, and multiple usage data similarly improved performance. Results for IoTDevID were strong with a 92.50 F1 score for 31 IP-only device classes, similar to our results on previous datasets. In all cases, the IoTDevID aggregation algorithm improved model performance. For non-IP devices we obtained a 78.80 F1 score for 40 device classes, though with much less data, confirming that data quantity is also important to model performance.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.08679
- https://arxiv.org/pdf/2307.08679
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384648606
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4384648606Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.08679Digital Object Identifier
- Title
-
Externally validating the IoTDevID device identification methodology using the CIC IoT 2022 DatasetWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-03Full publication date if available
- Authors
-
Kahraman Kostas, Mike Just, Michael A. LonesList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.08679Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.08679Direct 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
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https://arxiv.org/pdf/2307.08679Direct OA link when available
- Concepts
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Computer science, Identification (biology), Idle, Data mining, Set (abstract data type), Feature (linguistics), Network packet, Data set, Internet of Things, Machine learning, Artificial intelligence, Embedded system, Computer network, Operating system, Philosophy, Biology, Linguistics, Programming language, BotanyTop 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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2024: 2, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.device, | 86 |
| abstract_inverted_index.devices | 13, 198 |
| abstract_inverted_index.diverse | 98 |
| abstract_inverted_index.earlier | 73 |
| abstract_inverted_index.examine | 121 |
| abstract_inverted_index.explore | 112 |
| abstract_inverted_index.feature | 55 |
| abstract_inverted_index.impacts | 123 |
| abstract_inverted_index.machine | 27 |
| abstract_inverted_index.network | 34 |
| abstract_inverted_index.present | 41 |
| abstract_inverted_index.results | 132, 182 |
| abstract_inverted_index.several | 70 |
| abstract_inverted_index.similar | 179 |
| abstract_inverted_index.states. | 106 |
| abstract_inverted_index.testing | 50 |
| abstract_inverted_index.trained | 145, 152 |
| abstract_inverted_index.(benign) | 94 |
| abstract_inverted_index.Internet | 21 |
| abstract_inverted_index.IoTDevID | 18, 47, 166, 190 |
| abstract_inverted_index.approach | 29 |
| abstract_inverted_index.classes, | 178, 208 |
| abstract_inverted_index.dataset, | 110 |
| abstract_inverted_index.dataset. | 64 |
| abstract_inverted_index.devices, | 80 |
| abstract_inverted_index.example, | 143 |
| abstract_inverted_index.improved | 162, 193 |
| abstract_inverted_index.indicate | 133 |
| abstract_inverted_index.learning | 28 |
| abstract_inverted_index.multiple | 81, 158 |
| abstract_inverted_index.obtained | 200 |
| abstract_inverted_index.previous | 184 |
| abstract_inverted_index.proposes | 25 |
| abstract_inverted_index.quantity | 217 |
| abstract_inverted_index.securing | 11 |
| abstract_inverted_index.validity | 114 |
| abstract_inverted_index.algorithm | 192 |
| abstract_inverted_index.datasets, | 74 |
| abstract_inverted_index.datasets. | 185 |
| abstract_inverted_index.diversity | 136 |
| abstract_inverted_index.features. | 36 |
| abstract_inverted_index.important | 138, 220 |
| abstract_inverted_index.including | 75 |
| abstract_inverted_index.instances | 82 |
| abstract_inverted_index.profiles, | 100 |
| abstract_inverted_index.similarly | 161 |
| abstract_inverted_index.IoTDevID's | 116 |
| abstract_inverted_index.advantages | 71 |
| abstract_inverted_index.algorithm, | 60 |
| abstract_inverted_index.confirming | 214 |
| abstract_inverted_index.validation | 43 |
| abstract_inverted_index.aggregation | 59, 191 |
| abstract_inverted_index.components, | 52, 118 |
| abstract_inverted_index.diagnosing, | 9 |
| abstract_inverted_index.independent | 109 |
| abstract_inverted_index.outperformed | 150 |
| abstract_inverted_index.performance. | 130, 141, 163, 195, 223 |
| abstract_inverted_index.recognizing, | 8 |
| abstract_inverted_index.(CIC-IoT-2022) | 68 |
| abstract_inverted_index.identification | 32 |
| abstract_inverted_index.proliferation, | 7 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |