A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things Article Swipe
S. V. N. Santhosh Kumar
,
M. Selvi
,
A. Kannan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1155/2023/8981988
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1155/2023/8981988
The Internet of Things (IoT) is a distributed system which is made up of the connections of smart objects (things) that can continuously sense the events in their sensing domain and transmit the data via the Internet. IoT is considered as the next revolution of the Internet since it has provided vast improvements in day‐to‐day activities of humans including the provision of efficient healthcare services and development of smart cities and intelligent transport systems. The IoT environment, by the application of suitable security mechanisms through efficient security management techniques, intrusion detection systems provide a wall of defence against the attacks on the Internet and on the devices connected with Internet by effective monitoring of the Internet traffic. Therefore, the intrusion detection system (IDS) is a resolution proposed by the researchers to monitor and secure the IoT communication. In this work, a meticulous analysis of the security of IoT networks based on quality‐of‐service metrics is performed for deploying intrusion detection systems by carrying out experiments on secured communication and measuring the network’s performance based on comparing them with the existing security metrics. Finally, we propose a new and effective IDS using a deep learning‐based classification approach, namely, fuzzy CNN, for improving the security of communication. The major and foremost advantages of this system include an upsurge in detection accuracy, the accurate detection of denial of service (DoS) attacks more efficiently, and the reduction of false positive rates.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2023/8981988
- https://downloads.hindawi.com/journals/cin/2023/8981988.pdf
- OA Status
- hybrid
- Cited By
- 110
- References
- 134
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4318261440Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2023/8981988Digital Object Identifier
- Title
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A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of ThingsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
-
S. V. N. Santhosh Kumar, M. Selvi, A. KannanList of authors in order
- Landing page
-
https://doi.org/10.1155/2023/8981988Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/cin/2023/8981988.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/cin/2023/8981988.pdfDirect OA link when available
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Computer science, Intrusion detection system, Denial-of-service attack, Computer security, The Internet, Internet of Things, Computer network, World Wide WebTop concepts (fields/topics) attached by OpenAlex
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110Total citation count in OpenAlex
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2025: 45, 2024: 41, 2023: 24Per-year citation counts (last 5 years)
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134Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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