An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks Article Swipe
Yajing Wang
,
Ma Juan
,
Ashutosh Sharma
,
Pradeep Kumar Singh
,
Gurjot Singh Gaba
,
Mehedi Masud
,
Mohammed Baz
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/5558860
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/5558860
Intrusion detection is crucial in computer network security issues; therefore, this work is aimed at maximizing network security protection and its improvement by proposing various preventive techniques. Outlier detection and semisupervised clustering algorithms based on shared nearest neighbors are proposed in this work to address intrusion detection by converting it into a problem of mining outliers using the network behavior dataset. The algorithm uses shared nearest neighbors as similarity, judges whether it is an outlier according to the number of nearest neighbors of a data point, and performs semisupervised clustering on the dataset where outliers are deleted. In the process of semisupervised clustering, vast prior knowledge is added, and the dataset is clustered according to the principle of graph segmentation. The novelty of the proposed algorithm lies in outlier detection while effectively avoiding the dependence on parameters, thus eliminating the influence of outliers on clustering. This article uses real datasets: lypmphography and glass for simulation purposes. The simulation results show that the algorithm proposed in this paper can effectively detect outliers and has a good clustering effect. Furthermore, the experimentation reveals that the outlier detection‐based SCA‐SNN algorithm has the best practical effect on the dataset without outliers, clearly validating the clustering performance of the outlier detection‐based SCA‐SNN algorithm. Furthermore, compared to the other state‐of‐the‐art anomaly detection method, it was revealed that the anomaly detection technology based on outlier mining does not require a training process. Thus, they overcome the current anomaly detection problems caused due to incomplete normal patterns in training samples.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2021/5558860
- https://downloads.hindawi.com/journals/js/2021/5558860.pdf
- OA Status
- hybrid
- Cited By
- 52
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3170510247
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3170510247Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2021/5558860Digital Object Identifier
- Title
-
An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Yajing Wang, Ma Juan, Ashutosh Sharma, Pradeep Kumar Singh, Gurjot Singh Gaba, Mehedi Masud, Mohammed BazList of authors in order
- Landing page
-
https://doi.org/10.1155/2021/5558860Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/js/2021/5558860.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/js/2021/5558860.pdfDirect OA link when available
- Concepts
-
Outlier, Anomaly detection, Cluster analysis, Computer science, Data mining, Intrusion detection system, DBSCAN, Local outlier factor, Artificial intelligence, Pattern recognition (psychology), Network security, CURE data clustering algorithm, Fuzzy clustering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
52Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 4, 2023: 13, 2022: 24, 2021: 5Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.it | 49, 71, 217 |
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| abstract_inverted_index.and | 19, 29, 86, 108, 151, 171 |
| abstract_inverted_index.are | 38, 95 |
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