Exploring the Integration of Artificial Intelligence in Network Traffic Monitoring in Federal University, Wukari, Taraba State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
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- Recommended for :
- NGN 5000
Background of the Study
As universities increasingly depend on networked systems for daily operations, monitoring and managing network traffic have become critical for preventing performance issues and ensuring security. Federal University, Wukari, Taraba State, is no exception. Efficient network traffic monitoring helps administrators detect potential threats, bottlenecks, and unauthorized usage (Musa et al., 2024).
Traditional network monitoring tools rely heavily on manual configuration and analysis, which can be time-consuming and prone to human error. With the advent of Artificial Intelligence (AI), network monitoring has seen a shift toward more automated and intelligent systems. AI techniques, including machine learning (ML) and deep learning (DL), can be leveraged to analyze vast amounts of network traffic data, identify patterns, and detect anomalies in real-time, making network management more efficient (Ogunbiyi et al., 2023).
This study explores the integration of AI in network traffic monitoring at Federal University, Wukari, aiming to assess its potential benefits and challenges for optimizing network performance and enhancing security.
Statement of the Problem
Federal University, Wukari, faces challenges in effectively monitoring network traffic and detecting malicious activities due to the large volume of data transmitted across its systems. Traditional monitoring methods are inadequate for real-time detection of sophisticated attacks such as Distributed Denial of Service (DDoS) and botnet activities, leading to network downtime and performance degradation.
This study seeks to explore the potential of integrating AI technologies into network traffic monitoring to improve real-time detection and response to network anomalies.
Objectives of the Study
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To evaluate the current network traffic monitoring techniques used at Federal University, Wukari.
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To assess the feasibility of integrating AI technologies into the university’s network monitoring system.
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To propose AI-driven solutions for enhancing network traffic analysis and security at Federal University, Wukari.
Research Questions
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What network traffic monitoring techniques are currently employed at Federal University, Wukari?
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How can AI technologies improve the detection of anomalies and threats in network traffic?
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What are the challenges and benefits of implementing AI for network traffic monitoring in a university environment?
Significance of the Study
This study will assist Federal University, Wukari, in adopting advanced AI techniques for network traffic monitoring, potentially improving network performance and security. The findings will help optimize resource usage, detect security breaches faster, and reduce downtime in the university’s digital infrastructure.
Scope and Limitations of the Study
The study will focus solely on AI-based network traffic monitoring within Federal University, Wukari, Taraba State, and will not address other aspects of network management.
Definitions of Terms
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Network Traffic Monitoring: The process of overseeing and analyzing data packets transmitted across a network to ensure optimal performance and security.
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Artificial Intelligence (AI): A branch of computer science that simulates human intelligence in machines.
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Anomaly Detection: The identification of patterns in data that do not conform to expected behavior, often used to detect security breaches or network malfunctions.
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