Exploring the Use of Machine Learning in Network Traffic Analysis in Federal University, Wukari, Taraba State
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Background of the Study
The exponential growth in network traffic has necessitated advanced methods for traffic analysis to improve performance, security, and resource allocation. Traditional network monitoring techniques rely on predefined rules and signature-based detection, which are often insufficient in identifying sophisticated network anomalies. Machine learning (ML) offers a data-driven approach by analyzing patterns, detecting anomalies, and predicting network behavior based on historical data.
Federal University, Wukari, generates substantial network traffic from academic, research, and administrative activities. Effective network traffic analysis is crucial for identifying security threats, optimizing bandwidth usage, and preventing network congestion. Machine learning techniques such as anomaly detection, clustering, and classification can enhance network traffic analysis by providing real-time insights into traffic patterns and potential threats.
This study explores the application of machine learning in network traffic analysis at Federal University, Wukari, evaluating its effectiveness in detecting anomalies and improving network performance.
Statement of the Problem
Traditional network traffic analysis methods struggle to detect sophisticated cyber threats and anomalies in large-scale networks. Federal University, Wukari, lacks an advanced network monitoring system capable of identifying emerging threats and optimizing bandwidth usage. Manual traffic analysis is inefficient and fails to provide real-time insights, leading to security vulnerabilities and performance issues.
Machine learning techniques can enhance network traffic analysis by learning from past network behaviors, detecting unusual patterns, and predicting potential security incidents. However, the adoption and effectiveness of machine learning in network analysis at Federal University, Wukari, remain unexplored. This study seeks to bridge this gap by assessing the role of machine learning in improving network traffic analysis.
Objectives of the Study
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To evaluate the effectiveness of machine learning in analyzing network traffic at Federal University, Wukari.
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To identify key challenges in implementing machine learning-based network traffic analysis.
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To propose strategies for optimizing machine learning in network traffic monitoring.
Research Questions
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How effective is machine learning in detecting anomalies in network traffic?
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What challenges hinder the adoption of machine learning for network traffic analysis?
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What strategies can enhance the effectiveness of machine learning in network monitoring?
Scope and Limitations of the Study
This study is limited to the application of machine learning in network traffic analysis at Federal University, Wukari, Taraba State. Limitations include data availability and model training complexity.
Definitions of Terms
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Anomaly Detection: Identifying patterns in network traffic that deviate from normal behavior.
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Bandwidth Optimization: The process of managing network resources to enhance performance.
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Clustering Algorithm: A machine learning technique that groups similar data points based on patterns.
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