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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 the method, the authors 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.

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