Development of an AI-Based Malware Detection System in Federal University, Lokoja, Kogi State
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- NGN 5000
Background of the Study
Cyber threats have become increasingly sophisticated, with malware attacks posing significant risks to university networks. Malware can compromise sensitive information, disrupt academic activities, and result in financial losses (Bello & Nwafor, 2024). Traditional signature-based malware detection techniques are no longer sufficient due to the emergence of polymorphic and zero-day attacks.
AI-based malware detection systems leverage machine learning algorithms to analyze patterns, detect anomalies, and identify malicious activities in real time. These systems offer improved accuracy, faster threat detection, and proactive mitigation strategies (Chukwu et al., 2023).
This study focuses on developing an AI-based malware detection system for Federal University, Lokoja, to enhance network security by detecting and mitigating malware threats effectively.
Statement of the Problem
Federal University, Lokoja, faces frequent cyber threats due to inadequate malware detection mechanisms. Existing security solutions rely on traditional antivirus programs, which struggle to detect evolving malware variants. The lack of an intelligent threat detection system increases the risk of data breaches and system downtime.
This study proposes an AI-based malware detection system that utilizes machine learning techniques to enhance security by detecting and mitigating malware threats in real-time.
Objectives of the Study
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To assess the current malware threats affecting Federal University, Lokoja.
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To develop an AI-based malware detection system for proactive threat identification.
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To evaluate the system’s effectiveness in detecting and preventing malware attacks.
Research Questions
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What are the major malware threats affecting university networks?
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How can AI-based techniques improve malware detection efficiency?
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How effective is the proposed system in mitigating malware threats?
Significance of the Study
This study will contribute to university cybersecurity by developing an AI-based malware detection system that enhances real-time threat identification. The findings will benefit IT administrators and cybersecurity professionals in mitigating malware-related risks.
Scope and Limitations of the Study
The study focuses on developing an AI-based malware detection system for Federal University, Lokoja, Kogi State. It does not explore other AI applications in cybersecurity beyond malware detection.
Definitions of Terms
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Malware: Malicious software designed to damage, disrupt, or gain unauthorized access to computer systems.
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Machine Learning: A subset of AI that enables systems to learn from data patterns and improve performance.
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Zero-Day Attack: A cyberattack that exploits a previously unknown vulnerability.
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