Development of a Model for Network Activity Monitoring on Active User

ET MICHAEL; Prof ND NWIABU; KC PAUL

doi:10.56201/ijcsmt.vol.11.no10.2025.pg12.22

📄 Download Paper

Development of a Model for Network Activity Monitoring on Active User

ET MICHAEL, Prof ND NWIABU, KC PAUL

Abstract

This study examines the development of a model to monitor the activities of active users in a network. The project presents a comprehensive approach for a network monitoring system focused on enhancing security by analyzing user behavior. The system employs advanced algorithms to monitor real-time network traffic, identifying potential threats and unusual activities that deviate from established user behavior patterns. By integrating anomaly detection and machine learning techniques, the system provides timely alerts and insights into security incidents, enabling rapid response to potential breaches. Case studies illustrate its effectiveness in detecting insider threats, unauthorized access, and malware activities. Ultimately, this monitoring system aims to fortify network security by providing tools needed to safeguard sensitive information and maintain operational integrity. The network monitoring system collects data from various sources, including routers, switches, and firewalls, to gather real-time traffic statistics. These metrics include bandwidth usage, packet transmission rates, and connection frequency. User behavior analyzed based on historical patterns, with deviations from typical behavior being flagged as potential anomalies. The system uses supervised machine learning algorithms trained on labeled traffic data to classify active users’ sessions detect possible security breaches or performance bottleneck. The system successfully identified irregular activity patterns for 95% of the anomalies in the test environment, with a precision rate 92% in distinguishing active user’s traffic from potential malicious traffic. The network performance improved by 18%, thanks to real-time resource optimization insights provided by the monitoring system. Additionally, the anomaly detection algorithm reduced false positive by 30% compared to traditional monitoring methods. The results demonstrate that proactive network monitor

Keywords

NetworkMalwareBandwidthAnomaliesMaliciousSecurityMonitor.

References

Ahmed et, al. (2019). “A Survey of Network Traffic Analysis Technique”. Alavi & Grispos (2021). Frameworks that Emphasize User Consent and Data Anonymization, 120 - 145 Chen D. M. (2011). Developed Heuristics to Analyze Characteristics and Correlations Between Inter-data Centers and Client Traffic, 48 - 72 Easley, D. & Jon, K. (2010). Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Englander, I., (2013). The Architecture of Computer Hardware, Systems Software & Networking, Book, Fourth Edition. Fang, W., Zhijin, Z. & Xueyi, Y., (2008). A New Dynamic Network Monitoring Based on IA. International Symposium on Computer Science and Computational Technology, IEEE, 2, 637 - 640. Feldkuhn, L. & Erickson, J., (1989). Event management as a common functional area of open systems management. Proceedings of the First IFIP Symposium on Integrated Network Management, 365-376. Hossian & Kaushik, D. (2020). A Hybrid Deep Learning Frame-work for Daily Living Human. Activity Recognition With Cluster-based video Summarization. 208-215. http://agile.csc.ncsu.edu/SEMaterials/UMLOverview.pdf. http://www.itqlick.com/spiceworks/feedback http://www.networkmanagementsoftware.com/network-management-software- smackdown http://www.service-desk.co/pdf/opmanagerproduct-overview.pdf http://www.techopedia.com/definition/20974/network-management Hzubiidi, T. G. & Keerthhan, K. (2020). Performance Evaluation of Machine Learning Models for Network Traffic Monitoring, 335-348. Kaspersky Lab., (2013). Global Corporate IT Security Risks. Keening, et al. (2001). Analyzed the Dedication of Insider Threats by Monitoring system Call Activities. Kumar, et al. (2020). Integration With Security Information and Event Management System. Kyenning, N. N. (2001). Analyzing the Detection of Inside Threats by Monitoring System call Activities, 235 -342 Michalski, M., (2009). A Software and Hardware System for a Fully Functional Remote Access to Laboratory Networks. Fifth International Conference on Networking and Services, IEEE, 561 – 565. Moustafa et, al. (2019). “Network traffic Analysis for Security”. Peterson, G. G. (2022). Challenges of Tracking User Activity Across Cloud Services and Mobile Applications, 115 – 122. Pham et, al. ( 2021). “Enhancing Network Intrusion Detection with Assemble Learning. Richard, T. & Watson, (2007). Information Systems, University of Georgia. Rosenberg, D. & Scott, K. (1999), Use Case Driven Object Modeling with UML: A Practical Approach, Molecular Informatics, Massachusetts, Addison-Wesley. Sloman, M. & Jonathan, D., (1994). Policy Conflict Analysis in Journal of Organizational Computing, 4(1), 1-22. Sloman, M. (1994). Networks and Distributed Systems Management, Addison Wesley Longman Publishing Co., Inc., Boston, MA, USA. Stephen, P. O. & Kirby, B., (2007). Asterisk for Dummies, chapter 10. Suri, S. & Batra, V., (2010). Comparative Study of Network Monitoring Tools. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 1(3), 63-65. Trimintzios, P., Polychronakis, M., Papadogiannakis, A., Foukarakis, M., Markatos, E. P. & Oslebo, A., (2006). DiMAPI: An application programming interface for distributed network monitoring. Conference on Network Operations and Management Symposium, IEEE, 382-393. Zhang & David, H. C. (2018). Efficient network monitoring with distributed system, 112 – 165 Zhou, X. W. (2021). Distributed Architecture which can Measure and Monitor Online Internet Traffic, 375-382

Development of a Model for Network Activity Monitoring on Active User

Abstract

Keywords

References

More Articles from INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY