SCNTA Article Swipe
Romil Rawat
,
Bhagwati Garg
,
Kiran Pachlasiya
,
Vinod Mahor
,
Shrikant Telang
,
Mukesh Chouhan
,
Surendra Kumar Shukla
,
Rina Mishra
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.4018/ijitwe.297971
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.4018/ijitwe.297971
Real-time network inspection applications face a threat of vulnerability as high-speed networks continue to expand. For companies and ISPs, real-time traffic classification is an issue. The classifier monitor is made up of three modules: Capturing_of_Packets (CoP) and pre-processing, Reconciliation_of_Flow (RoF), and categorization of Machine Learning (ML). Based on parallel processing along with well-defined interfacing of data, the modules are framed, allowing each module to be modified and upgraded separately. The Reconciliation_of_Flow (RoF) mechanism becomes the output bottleneck in this pipeline. In this implementation, an optimal reconciliation process was used, resulting in an average delivery time of 0.62 seconds. In order to verify our method, we equated the results of the AdaBoost Ensemble Learning Algorithm (ABELA), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), and Flexible Naive Bayes (FNB) in the classification module. The architectural design of the run time CSNTA categorization (flow-based) scheme is presented in this paper.
Related Topics
Concepts
Computer science
Naive Bayes classifier
Bottleneck
Machine learning
Artificial intelligence
AdaBoost
Interfacing
Decision tree
Pipeline (software)
Network packet
Boosting (machine learning)
Scheme (mathematics)
Data mining
Classifier (UML)
Support vector machine
Embedded system
Computer hardware
Operating system
Computer network
Mathematical analysis
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4018/ijitwe.297971
- https://www.igi-global.com/ViewTitle.aspx?TitleId=297971&isxn=9781799894001
- OA Status
- diamond
- Cited By
- 10
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213365588
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4213365588Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.4018/ijitwe.297971Digital Object Identifier
- Title
-
SCNTAWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-23Full publication date if available
- Authors
-
Romil Rawat, Bhagwati Garg, Kiran Pachlasiya, Vinod Mahor, Shrikant Telang, Mukesh Chouhan, Surendra Kumar Shukla, Rina MishraList of authors in order
- Landing page
-
https://doi.org/10.4018/ijitwe.297971Publisher landing page
- PDF URL
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https://www.igi-global.com/ViewTitle.aspx?TitleId=297971&isxn=9781799894001Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://www.igi-global.com/ViewTitle.aspx?TitleId=297971&isxn=9781799894001Direct OA link when available
- Concepts
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Computer science, Naive Bayes classifier, Bottleneck, Machine learning, Artificial intelligence, AdaBoost, Interfacing, Decision tree, Pipeline (software), Network packet, Boosting (machine learning), Scheme (mathematics), Data mining, Classifier (UML), Support vector machine, Embedded system, Computer hardware, Operating system, Computer network, Mathematical analysis, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1, 2023: 4, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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