SCNTA: Monitoring of Network Availability and Activity for Identification of Anomalies Using Machine Learning Approaches

Romil Rawat, Bhagwati Garg, Kiran Pachlasiya, Vinod Mahor, Shrikant Telang, Mukesh Chouhan, Surendra Kumar Shukla, Rina Mishra

Source Title: International Journal of Information Technology and Web Engineering (IJITWE)17(1)

ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781799894001|DOI: 10.4018/IJITWE.297971

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MLA

Rawat, Romil, et al. "SCNTA: Monitoring of Network Availability and Activity for Identification of Anomalies Using Machine Learning Approaches." IJITWE vol.17, no.1 2022: pp.1-19. http://doi.org/10.4018/IJITWE.297971

APA

Rawat, R., Garg, B., Pachlasiya, K., Mahor, V., Telang, S., Chouhan, M., Shukla, S. K., & Mishra, R. (2022). SCNTA: Monitoring of Network Availability and Activity for Identification of Anomalies Using Machine Learning Approaches. International Journal of Information Technology and Web Engineering (IJITWE), 17(1), 1-19. http://doi.org/10.4018/IJITWE.297971

Chicago

Rawat, Romil, et al. "SCNTA: Monitoring of Network Availability and Activity for Identification of Anomalies Using Machine Learning Approaches," International Journal of Information Technology and Web Engineering (IJITWE) 17, no.1: 1-19. http://doi.org/10.4018/IJITWE.297971

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Abstract

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.