Optimization of Network Security Policies Using AI-Driven Automation in Federal University, Birnin Kebbi, Kebbi State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
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- NGN 5000
Background of the Study
In the digital age, universities like Federal University Birnin Kebbi in Kebbi State rely heavily on networked systems to support academic and administrative functions. With the increase in cyber threats targeting universities, securing network infrastructure has become a paramount concern. Traditional network security measures, such as firewalls and antivirus software, are often insufficient in detecting and mitigating complex, evolving threats in real time. Therefore, there is a growing interest in leveraging Artificial Intelligence (AI) to enhance network security.
AI-driven automation has shown great promise in optimizing network security policies by enabling systems to autonomously detect, prevent, and respond to security incidents. Machine learning algorithms, for example, can analyze vast amounts of network traffic data, identify patterns of malicious behavior, and automatically adjust security settings to counter emerging threats. The application of AI-driven automation in network security policies could revolutionize the way universities manage their network security and mitigate risks associated with cyber threats. However, its practical implementation in Nigerian universities, particularly in Kebbi State, remains underexplored.
Statement of the Problem
Federal University Birnin Kebbi, despite its reliance on networked systems, has not fully optimized its network security policies with AI-driven automation. The existing security infrastructure is largely manual and reactive, relying on traditional methods that are inadequate for addressing modern, sophisticated cyber threats. The lack of an AI-based system for proactive threat detection, response, and policy optimization exposes the university’s network to vulnerabilities, potentially compromising sensitive academic and administrative data. Therefore, there is a need to explore the potential of AI-driven automation to optimize network security policies at Federal University Birnin Kebbi.
Objectives of the Study
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To explore the potential benefits of integrating AI-driven automation in optimizing network security policies at Federal University Birnin Kebbi.
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To develop an AI-based system for automating the detection and mitigation of network security threats.
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To evaluate the effectiveness of AI-driven automation in improving the response time and accuracy of network security policies.
Research Questions
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How can AI-driven automation optimize network security policies at Federal University Birnin Kebbi?
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What are the key features and algorithms required for effective AI-driven network security automation?
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How does the performance of AI-based automation compare to traditional manual network security measures in terms of threat detection and response?
Significance of the Study
The study is significant in advancing the understanding of AI's role in network security within the context of Nigerian universities. By optimizing network security policies with AI-driven automation, Federal University Birnin Kebbi can improve its resilience against cyber threats, reduce human error in security management, and enhance the overall efficiency of its security operations. The findings will provide insights into the application of AI in securing academic and administrative networks, benefiting universities both in Kebbi State and across Nigeria.
Scope and Limitations of the Study
The study will focus on the application of AI-driven automation to optimize network security policies at Federal University Birnin Kebbi. The research will primarily evaluate the effectiveness of AI in detecting, mitigating, and responding to network security threats in real-time. The study will be limited to the university’s network infrastructure and will not extend to other institutions in Kebbi State or beyond.
Definitions of Terms
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Artificial Intelligence (AI): The simulation of human intelligence in machines that can analyze data, learn from it, and make decisions.
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Network Security Policies: Rules and guidelines established to protect the network infrastructure and data from unauthorized access or cyber-attacks.
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Automation: The use of technology to perform tasks without human intervention, often driven by algorithms or artificial intelligence.
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Machine Learning: A subset of AI that enables systems to learn from data patterns and make decisions without explicit programming.
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