Binary Chimp Optimization Algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor Networks Article Swipe
Mohammed Aljebreen
,
Manal Abdullah Alohali
,
Muhammad Kashif Saeed
,
Heba Mohsen
,
Mesfer Al Duhayyim
,
Amgad Atta Abdelmageed
,
Suhanda Drar
,
Sitelbanat Abdelbagi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23084073
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23084073
An Internet of Things (IoT)-assisted Wireless Sensor Network (WSNs) is a system where WSN nodes and IoT devices together work to share, collect, and process data. This incorporation aims to enhance the effectiveness and efficiency of data analysis and collection, resulting in automation and improved decision-making. Security in WSN-assisted IoT can be referred to as the measures initiated for protecting WSN linked to the IoT. This article presents a Binary Chimp Optimization Algorithm with Machine Learning based Intrusion Detection (BCOA-MLID) technique for secure IoT-WSN. The presented BCOA-MLID technique intends to effectively discriminate different types of attacks to secure the IoT-WSN. In the presented BCOA-MLID technique, data normalization is initially carried out. The BCOA is designed for the optimal selection of features to improve intrusion detection efficacy. To detect intrusions in the IoT-WSN, the BCOA-MLID technique employs a class-specific cost regulation extreme learning machine classification model with a sine cosine algorithm as a parameter optimization approach. The experimental result of the BCOA-MLID technique is tested on the Kaggle intrusion dataset, and the results showcase the significant outcomes of the BCOA-MLID technique with a maximum accuracy of 99.36%, whereas the XGBoost and KNN-AOA models obtained a reduced accuracy of 96.83% and 97.20%, respectively.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23084073
- https://www.mdpi.com/1424-8220/23/8/4073/pdf?version=1681810362
- OA Status
- gold
- Cited By
- 23
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366280065
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4366280065Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23084073Digital Object Identifier
- Title
-
Binary Chimp Optimization Algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-18Full publication date if available
- Authors
-
Mohammed Aljebreen, Manal Abdullah Alohali, Muhammad Kashif Saeed, Heba Mohsen, Mesfer Al Duhayyim, Amgad Atta Abdelmageed, Suhanda Drar, Sitelbanat AbdelbagiList of authors in order
- Landing page
-
https://doi.org/10.3390/s23084073Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/8/4073/pdf?version=1681810362Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/23/8/4073/pdf?version=1681810362Direct OA link when available
- Concepts
-
Computer science, Wireless sensor network, Intrusion detection system, Algorithm, Internet of Things, Wireless, Automation, Machine learning, Data mining, Artificial intelligence, Computer network, Embedded system, Engineering, Mechanical engineering, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
23Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 11, 2023: 2Per-year citation counts (last 5 years)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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