Optimization of Network Security Using Artificial Intelligence in Federal University, Birnin Kebbi, Kebbi State
- Project Research
- 1-5 Chapters
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- NGN 5000
Background of the Study:
The rapid digital transformation in higher education institutions has increased the reliance on networked systems for research, communication, and administration. However, this expansion has also exposed institutions like Federal University, Birnin Kebbi, to a growing number of cyber threats, including malware, unauthorized access, and denial-of-service attacks (Sharma et al., 2023). Traditional security approaches, such as firewalls and signature-based intrusion detection systems, struggle to keep pace with evolving cyber threats. Artificial Intelligence (AI) has emerged as a powerful tool in cybersecurity, offering adaptive and intelligent security solutions capable of identifying threats in real time (Mehta & Patel, 2024).
AI-driven security solutions leverage machine learning (ML) and deep learning (DL) models to detect anomalies, automate threat response, and enhance the overall resilience of network infrastructures. By implementing AI-based security mechanisms, Federal University, Birnin Kebbi, can significantly improve its ability to detect and mitigate cyber threats, reducing the risk of security breaches.
Statement of the Problem:
The traditional cybersecurity measures implemented at Federal University, Birnin Kebbi, are reactive and often insufficient in countering advanced cyber threats. These systems rely on predefined signatures, making them ineffective against new and evolving attack techniques. The university faces risks such as unauthorized access, data breaches, and service disruptions, which threaten its academic and administrative operations. The absence of AI-driven security mechanisms limits the institution’s ability to predict and respond to threats in real time. Therefore, there is a need to optimize the university’s network security using AI to enhance threat detection and mitigation capabilities.
Objectives of the Study:
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To analyze the current network security challenges at Federal University, Birnin Kebbi.
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To develop an AI-based security model for real-time threat detection and response.
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To evaluate the effectiveness of AI-driven network security solutions compared to traditional security approaches.
Research Questions:
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What are the existing security vulnerabilities in the university’s network infrastructure?
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How can AI-based models enhance real-time threat detection and response in the university’s cybersecurity framework?
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What are the comparative advantages of AI-driven security solutions over conventional cybersecurity approaches?
Significance of the Study:
This study will provide an AI-driven approach to improving network security at Federal University, Birnin Kebbi, by leveraging machine learning techniques for threat detection. The findings will help the university adopt proactive security measures, reducing the risk of cyberattacks. Additionally, the research will contribute to the growing field of AI in cybersecurity, offering insights into its application in educational institutions.
Scope and Limitations of the Study:
The study focuses on optimizing network security using AI techniques in Federal University, Birnin Kebbi, Kebbi State. The research is limited to AI-based intrusion detection, threat analysis, and automated response mechanisms, excluding other cybersecurity areas such as physical security measures.
Definitions of Terms:
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Artificial Intelligence (AI): A branch of computer science that enables machines to perform tasks that typically require human intelligence.
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Machine Learning (ML): A subset of AI that allows systems to learn from data patterns and improve their performance over time.
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Intrusion Detection System (IDS): A security mechanism used to monitor network traffic and detect unauthorized activities.
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