Investigation into the Effectiveness of AI-Based Network Security Solutions in Federal University, Gashua, Yobe State
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- NGN 5000
Background of the Study
Artificial Intelligence (AI) has revolutionized network security by enabling automated threat detection, real-time monitoring, and predictive analytics. AI-based security solutions leverage machine learning algorithms to analyze network traffic, detect anomalies, and prevent cyber threats before they escalate. These systems are particularly effective against evolving threats such as ransomware, phishing, and advanced persistent threats (APTs).
Federal University, Gashua, faces increasing cybersecurity risks due to the proliferation of cyber threats targeting academic institutions. Traditional security measures, such as firewalls and antivirus software, are often reactive and fail to detect zero-day attacks. AI-powered security solutions provide an adaptive defense mechanism that can learn from historical data and proactively identify suspicious activities.
Despite the advantages of AI-driven security systems, their adoption in university environments remains limited due to concerns about implementation costs, integration complexities, and false-positive detections. This study investigates the effectiveness of AI-based network security solutions in protecting the university’s digital infrastructure.
Statement of the Problem
Federal University, Gashua, relies on conventional security tools that struggle to combat sophisticated cyber threats. The increasing number of cyberattacks, data breaches, and unauthorized access incidents underscores the need for a more intelligent security framework. Traditional security approaches often generate a high number of false alerts, overwhelming security administrators.
AI-based network security solutions offer promise in enhancing threat detection accuracy and automating incident response. However, their effectiveness in university networks has not been thoroughly evaluated. This study seeks to assess the efficiency, accuracy, and reliability of AI-powered security solutions in mitigating cyber threats at Federal University, Gashua.
Objectives of the Study
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To evaluate the effectiveness of AI-based network security solutions in detecting and preventing cyber threats.
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To assess the accuracy and false-positive rates of AI-driven security systems.
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To analyze the challenges associated with implementing AI-based security solutions in a university environment.
Research Questions
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How effective are AI-based security solutions in detecting and mitigating cyber threats?
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What are the false-positive rates of AI-powered network security systems?
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What implementation challenges are associated with AI-driven security frameworks?
Scope and Limitations of the Study
This study focuses on evaluating AI-based network security at Federal University, Gashua, Yobe State. It will assess threat detection efficiency and system reliability. Limitations include computational resource constraints and dataset availability.
Definitions of Terms
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AI-Based Security: The use of artificial intelligence to detect and prevent cyber threats.
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False Positives: Incorrect identification of benign activities as security threats.
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Advanced Persistent Threats (APTs): Prolonged cyberattacks targeting specific organizations.
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