Evaluation of Machine Learning-Based Intrusion Detection Systems in University Networks: A Case Study of Taraba State University (Jalingo LGA, Taraba State)
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- NGN 5000
Background of the Study
As universities increasingly rely on digital platforms and online learning systems, the security of their networks has become a critical concern. Taraba State University, located in Jalingo LGA, Taraba State, faces the growing challenge of protecting its network infrastructure from cyberattacks. Traditional security methods, such as firewalls and antivirus software, are often inadequate to detect advanced and evolving threats. Machine learning (ML) has emerged as a promising solution for network security, offering the ability to identify and respond to intrusions more effectively through anomaly detection and pattern recognition.
This study will evaluate the effectiveness of machine learning-based intrusion detection systems (IDS) in protecting university networks by using a case study approach at Taraba State University. The research will assess the accuracy, efficiency, and scalability of various machine learning models for detecting cyberattacks and unauthorized access to university systems.
Statement of the Problem
The traditional network security measures in place at Taraba State University are increasingly ineffective against sophisticated cyber threats. There is a need for a more robust and adaptive solution that can learn from network traffic patterns and identify anomalies in real-time. Machine learning has the potential to address these gaps, but its practical application in university network security requires thorough evaluation.
Objectives of the Study
1. To assess the performance of different machine learning algorithms in detecting network intrusions at Taraba State University.
2. To compare the effectiveness of machine learning-based IDS with traditional network security methods.
3. To explore the challenges and opportunities in implementing machine learning-based IDS in university networks.
Research Questions
1. How effective are machine learning-based intrusion detection systems in identifying network threats in university settings?
2. What machine learning algorithms perform best for detecting intrusions in the context of a university network?
3. How does the performance of machine learning-based IDS compare to traditional intrusion detection systems in university networks?
Research Hypotheses
1. Machine learning-based intrusion detection systems will significantly outperform traditional security measures in detecting network intrusions.
2. Specific machine learning models (e.g., Random Forest, SVM, Neural Networks) will be more effective than others in identifying network anomalies and intrusions in university networks.
3. The implementation of machine learning-based IDS will enhance the overall network security and reduce the risk of cyberattacks at Taraba State University.
Significance of the Study
This study will provide valuable insights into the application of machine learning for enhancing network security in universities. It will help Taraba State University strengthen its cybersecurity posture, safeguard sensitive academic and administrative data, and contribute to the growing body of research on AI-based network security in higher education institutions.
Scope and Limitations of the Study
The study will focus on evaluating machine learning-based intrusion detection systems specifically for university networks, using Taraba State University as a case study. It will not cover other aspects of university network management or broader cybersecurity issues beyond intrusion detection.
Definitions of Terms
• Machine Learning (ML): A branch of artificial intelligence that enables systems to learn from data and make predictions or decisions without being explicitly programmed.
• Intrusion Detection System (IDS): A system designed to monitor network traffic for suspicious activity and potential security threats.
• Anomaly Detection: A method of identifying unusual patterns or behaviors in network traffic that may indicate malicious activity.
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