An Investigation into the Use of AI-Driven Security Solutions for Campus Networks in Federal University, Gusau, Zamfara State
- Project Research
- 1-5 Chapters
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- NGN 5000
Background of the Study
Campus networks in universities are increasingly targeted by cybercriminals due to the vast amount of valuable data they contain and the open, often unprotected nature of campus networks. At Federal University Gusau, Zamfara State, cybersecurity has become a pressing issue as the institution experiences an increasing number of cyber threats. Traditional network security measures, such as firewalls and antivirus software, are no longer sufficient to keep up with the sophistication of modern cyber threats. As a result, many universities are turning to Artificial Intelligence (AI)-driven security solutions to enhance their network defenses.
AI-driven security solutions use machine learning algorithms and data analytics to detect anomalies, identify patterns of malicious behavior, and respond to potential security threats in real-time. These solutions can automatically adapt to evolving threats, provide intelligent insights, and significantly reduce the burden on human security administrators. However, there is limited research on the practical implementation and effectiveness of AI-driven security solutions in Nigerian universities, especially in the context of campus networks. This study seeks to explore the potential of AI-based security solutions to improve the security of Federal University Gusau’s campus network.
Statement of the Problem
Despite the increasing reliance on networked systems at Federal University Gusau, the campus network faces numerous security challenges, including unauthorized access, data breaches, and malware attacks. The university’s current security infrastructure, which relies heavily on traditional methods, is struggling to address these growing threats. The absence of AI-driven security solutions that can autonomously detect and mitigate security risks leaves the campus network vulnerable to cyberattacks. Therefore, an investigation into the use of AI-driven security solutions is needed to determine their potential to enhance the university’s network security posture.
Objectives of the Study
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To explore the use of AI-driven security solutions in securing campus networks at Federal University Gusau.
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To evaluate the effectiveness of AI-based security systems in detecting and mitigating network threats in real-time.
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To propose recommendations for implementing AI-driven security solutions in Nigerian universities.
Research Questions
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How can AI-driven security solutions be applied to enhance the security of campus networks at Federal University Gusau?
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What are the advantages and limitations of using AI-driven security solutions for campus network security?
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How do AI-based security solutions compare to traditional security measures in terms of threat detection and response?
Significance of the Study
This study will offer valuable insights into the application of AI-driven security solutions in protecting campus networks from emerging cybersecurity threats. By investigating the potential of AI-based systems at Federal University Gusau, the research will help strengthen the university’s network security and provide a framework for other Nigerian universities to adopt similar solutions.
Scope and Limitations of the Study
The research will focus on investigating AI-driven security solutions for campus network security at Federal University Gusau. The study will primarily examine how these solutions can be implemented and their effectiveness in detecting and mitigating security threats. Limitations include the potential challenges in adopting AI technologies due to budget constraints, lack of skilled personnel, and resistance to change within the institution.
Definitions of Terms
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AI-Driven Security Solutions: Security technologies that use artificial intelligence and machine learning to detect, analyze, and respond to security threats.
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Network Security: Measures taken to protect a computer network from unauthorized access, misuse, or attacks.
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Anomaly Detection: The process of identifying unusual patterns or behaviors in network traffic that could indicate a security threat.
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Machine Learning: A type of AI that enables systems to learn from data and improve their performance over time without being explicitly programmed.
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