Design of an AI-Based Phishing Detection System for Federal University, Lafia, Nasarawa State
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- 1-5 Chapters
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- NGN 5000
Background of the Study:
Phishing attacks remain one of the most prevalent forms of cybercrime, targeting individuals and institutions to steal sensitive information, such as login credentials, financial data, and personal information (Aziz et al., 2023). Federal University, Lafia, Nasarawa State, like many academic institutions, is highly susceptible to phishing attacks due to the large number of students, staff, and faculty interacting online. Phishing scams typically involve fraudulent emails or websites that impersonate legitimate sources to trick users into revealing confidential information. These attacks can have severe consequences, including identity theft, financial loss, and data breaches, undermining the trust in the institution’s digital platforms (Ali et al., 2024).
Traditional phishing detection systems, such as email filters and URL blacklists, are often inadequate because they rely on known patterns, which can be easily bypassed by sophisticated attackers using novel tactics (Nguyen & Nguyen, 2024). Artificial Intelligence (AI), particularly machine learning (ML) algorithms, has shown significant promise in detecting phishing attempts by analyzing patterns and anomalies in web traffic, emails, and URLs. AI can learn from large datasets, adapt to evolving phishing tactics, and improve its detection accuracy over time. Implementing an AI-based phishing detection system in the university’s network infrastructure would enhance its ability to identify phishing attacks in real time, reducing the risk to students, faculty, and staff.
Statement of the Problem:
Federal University, Lafia, Nasarawa State, faces increasing incidents of phishing attacks targeting students, faculty, and staff, posing a significant threat to the integrity and security of the university’s digital platforms. Current phishing detection methods at the university are reactive and inefficient, relying mainly on outdated signature-based systems. These methods are unable to effectively detect new and evolving phishing tactics. As a result, there is a need for a more robust and proactive solution, such as an AI-based phishing detection system, that can analyze large datasets, recognize patterns, and provide timely alerts to prevent potential damage from phishing attacks.
Objectives of the Study:
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To design an AI-based phishing detection system tailored for Federal University, Lafia, that can accurately detect phishing attacks in emails, websites, and URLs.
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To evaluate the effectiveness of the proposed AI-based system in detecting phishing attempts compared to traditional phishing detection methods.
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To implement the AI-based phishing detection system in the university's network environment and assess its real-world performance.
Research Questions:
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How can AI be used to improve the detection of phishing attacks at Federal University, Lafia?
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What are the comparative benefits of using AI-based phishing detection systems over traditional methods?
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How effective is the AI-based phishing detection system in detecting phishing attacks in real-time within the university’s network?
Significance of the Study:
This study is crucial for Federal University, Lafia, as it will provide an advanced, AI-driven solution to protect students, faculty, and staff from phishing attacks. The implementation of such a system will reduce the risks associated with phishing and help safeguard sensitive information within the university’s digital ecosystem. Moreover, the research will contribute to the broader academic community by showcasing the effectiveness of AI in enhancing cybersecurity measures in educational institutions.
Scope and Limitations of the Study:
The study focuses on designing and implementing an AI-based phishing detection system for Federal University, Lafia, Nasarawa State. The research is limited to phishing detection via emails, websites, and URLs, excluding other forms of cyber-attacks. The study is also constrained by the university’s existing network infrastructure and resources.
Definitions of Terms:
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AI (Artificial Intelligence): A branch of computer science that focuses on creating systems capable of performing tasks that normally require human intelligence, such as pattern recognition.
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Phishing: A fraudulent attempt to obtain sensitive information by disguising as a trustworthy entity in electronic communications.
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Machine Learning (ML): A subset of AI that involves training algorithms to recognize patterns in data and make predictions based on those patterns.
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