Cybersecurity Threats Detection In IoT Using Krill Based Deep Neural Network Stacked Auto Encoders Article Swipe
Pragati Rana
,
S. K. Chauhan
,
B P Patil
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-349982/v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-349982/v1
The Internet of things (IoT) has concerned much significance for some manufacturing sectors including clinical fields, co-ordinations following, savvy urban communities, and automobiles. Anyway as a worldview, it is sensitive to different sorts of cyber-attacks. Customary very good quality security resolutions for guarantee an IoT structure are not reasonable. This deduces clever organization-based security plans as AI arrangements ought to be made. In this work, we propose Cyber Security Threats recognition in IoT utilizing Krill Based Deep Neural Network Stacked Auto Encoders (KDNN-SAE). In our proposed approach, first, the information pre-processing measure was acted in the underlying development before isolating the dataset into two segments: preparing and test. At that point, flow-based features are extracted from the pre-processed information. By then, the properties to be utilized by the algorithms are chosen in the attribute determination utilizing the Genetic Algorithm (GA). At last, our methodology completes with the execution of the machine learning algorithm KDNN-SAE. The exploratory results show that the introduced method beats the existing techniques to different execution measures.
Related Topics
Concepts
Computer science
Internet of Things
Artificial neural network
Information flow
Autoencoder
Artificial intelligence
Botnet
Deep learning
Quality (philosophy)
Encoder
Point (geometry)
Drone
Computer security
Machine learning
Data mining
The Internet
World Wide Web
Linguistics
Philosophy
Geometry
Epistemology
Genetics
Operating system
Mathematics
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-349982/v1
- https://www.researchsquare.com/article/rs-349982/v1.pdf?c=1631877221000
- OA Status
- green
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4233280127
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4233280127Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-349982/v1Digital Object Identifier
- Title
-
Cybersecurity Threats Detection In IoT Using Krill Based Deep Neural Network Stacked Auto EncodersWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-14Full publication date if available
- Authors
-
Pragati Rana, S. K. Chauhan, B P PatilList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-349982/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-349982/v1.pdf?c=1631877221000Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-349982/v1.pdf?c=1631877221000Direct OA link when available
- Concepts
-
Computer science, Internet of Things, Artificial neural network, Information flow, Autoencoder, Artificial intelligence, Botnet, Deep learning, Quality (philosophy), Encoder, Point (geometry), Drone, Computer security, Machine learning, Data mining, The Internet, World Wide Web, Linguistics, Philosophy, Geometry, Epistemology, Genetics, Operating system, Mathematics, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2021-04-14 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2786070938, https://openalex.org/W2566425973, https://openalex.org/W2752291283, https://openalex.org/W2903220614, https://openalex.org/W6753074994, https://openalex.org/W6732952056, https://openalex.org/W2567480004, https://openalex.org/W2748840447, https://openalex.org/W2508015754, https://openalex.org/W2799114415, https://openalex.org/W2947802941, https://openalex.org/W2733964592, https://openalex.org/W6713134421, https://openalex.org/W6683163990, https://openalex.org/W2976414818, https://openalex.org/W3025880593, https://openalex.org/W6731243579, https://openalex.org/W2800912855, https://openalex.org/W2987485128, https://openalex.org/W2762776925, https://openalex.org/W2796394805, https://openalex.org/W2061068374, https://openalex.org/W2884984219, https://openalex.org/W3028069824, https://openalex.org/W2926701059, https://openalex.org/W2591712613, https://openalex.org/W2154605267, https://openalex.org/W2564822508, https://openalex.org/W2953384591, https://openalex.org/W2157546348, https://openalex.org/W2586432806, https://openalex.org/W2853755454, https://openalex.org/W2617931713 |
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