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 and Rina Mishra
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Romil Rawat: Shri Vaishnav Vidyapeeth Vishwavidyalaya, India
Bhagwati Garg: Union Bank of India, Gwalior, India
Kiran Pachlasiya: NRI Institute of Science and Technology, Bhopal, India
Vinod Mahor: IPS College of Technology and Management, Gwalior, India
Shrikant Telang: Shri Vaishnav Vidyapeeth Vishwavidyalaya, India
Mukesh Chouhan: Government Polytechnic College, Sheopur, India
Surendra Kumar Shukla: Graphic Era University (Deemed), Deharadun, India
Rina Mishra: Shri Vaishnav Vidyapeeth Vishwavidyalayaharda, India

International Journal of Information Technology and Web Engineering (IJITWE), 2022, vol. 17, issue 1, 1-19

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.

Date: 2022
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