Optimization of AI-Driven Cybersecurity Threat Detection Systems in Federal University, Lafia, Nasarawa State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
- Reference Style:
- Recommended for :
- NGN 5000
Background of the Study
Cybersecurity threats are becoming more sophisticated, making traditional threat detection systems increasingly ineffective. The use of Artificial Intelligence (AI) in threat detection systems is gaining momentum due to its ability to analyze vast amounts of data in real-time, detect anomalies, and predict emerging threats. At Federal University Lafia, Nasarawa State, the growing use of digital resources and online platforms has increased the vulnerability of its networks to cyberattacks, necessitating the need for advanced security systems. AI-driven threat detection systems have shown great potential in enhancing the university's cybersecurity defenses by automating the detection and response to potential threats.
AI techniques, such as machine learning and deep learning, can continuously learn from network traffic, user behavior, and historical data to identify patterns that may indicate a cyberattack. By doing so, these systems can detect anomalies that traditional methods might miss and automatically initiate preventive actions. However, the optimization of AI-driven systems for campus network security has not been thoroughly explored in Nigerian universities, especially at Federal University Lafia. This study seeks to optimize AI-based cybersecurity threat detection systems, tailoring them to the specific needs of the university.
Statement of the Problem
Despite the rapid growth of digital platforms at Federal University Lafia, its existing cybersecurity infrastructure lacks AI-driven capabilities that could enhance the detection of sophisticated threats. Traditional security measures, such as firewalls and signature-based systems, are not sufficient to address emerging threats. This study aims to optimize AI-based threat detection systems for the university, improving the ability to identify and mitigate cybersecurity risks in real-time.
Objectives of the Study
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To explore the potential of AI-driven threat detection systems in enhancing cybersecurity at Federal University Lafia.
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To optimize machine learning algorithms for real-time detection of cybersecurity threats at the university.
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To assess the effectiveness of the optimized AI-driven threat detection system in improving the university’s overall network security.
Research Questions
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How can AI-driven threat detection systems be optimized to address the specific cybersecurity challenges at Federal University Lafia?
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What machine learning techniques are most effective for detecting emerging cybersecurity threats in the university's network?
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How will the implementation of an optimized AI-driven threat detection system improve the university’s security posture?
Significance of the Study
This research will provide Federal University Lafia with an optimized AI-based threat detection system that can enhance its ability to protect against sophisticated cyber threats. The findings will also contribute to the broader adoption of AI-driven cybersecurity solutions in Nigerian universities, improving overall campus network security.
Scope and Limitations of the Study
The study will focus on optimizing AI-driven threat detection systems specifically for the campus network of Federal University Lafia. Limitations include the university's current infrastructure, which may not be fully compatible with AI-based systems, and the challenge of training AI models with limited data.
Definitions of Terms
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AI-Driven Threat Detection: The use of artificial intelligence to monitor, detect, and respond to potential cybersecurity threats in a network.
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Machine Learning: A branch of AI that allows systems to learn from data and improve their performance over time.
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Cybersecurity Threats: Potential malicious activities, such as hacking or malware, that pose a risk to an organization’s data and network.
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