Development of Visualization System Analysis and Network Traffic Pattern to Detect Anomalies and Potential Security Threats
Student: Abdulrahman Abdullahi Aliyu (Project, 2025)
Department of Computer Science and Information Technology
Federal University, Dutsin-Ma, Katsina State
Abstract
ABSTRACT
This project presents a machine learning-based system for detecting anomalies in network traffic. Using unsupervised learning techniques, particularly Isolation Forest, the system analyzes traffic patterns, identifies deviations from normal behavior, and visualizes the findings. The approach enhances early detection of potential threats and supports network security monitoring. traffic has become essential to ensuring the integrity, confidentiality, and availability of digital infrastructure. Traditional detection systems, which often rely on signature-based or supervised learning techniques, fall short in identifying zero-day attacks and previously unknown threats. This project proposes the development of a visualization-driven network anomaly detection system that utilizes unsupervised learning techniques to detect suspicious patterns and activities within network traffic.
Keywords
For the full publication, please contact the author directly at: abdulrahmanaleeyu91@gmail.com
Filters
Institutions
- Covenant Polytechnic, Aba, Abia State 1
- Covenant University, Canaan Land, Ota, Ogun State 4
- Crawford University of Apostolic Faith Mission Faith City, Igbesa, Ogun State 2
- Crescent University, Abeokuta, Ogun State 1
- Cross Rivers University of Technology, Calabar, Cross Rivers State 142
- Delta State Polytechnic, Ogwashi-Uku, Delta State 11
- Delta State Polytechnic, Otefe, Delta State 12
- Delta State University, Abraka, Delta State 138
- Ebonyi State University, Abakaliki, Ebonyi State 17
- Edo University, Iyamho, Edo State 10