Enhancing University Digital Security with AI-Based Intrusion Detection Systems in Kebbi State University of Science and Technology, Aliero, Kebbi State
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Background of the Study
Digital security is a crucial aspect of modern university administration, as academic institutions increasingly rely on digital platforms for student records, academic resources, communication, and financial transactions. Cybersecurity threats, such as data breaches, hacking, and unauthorized access to sensitive information, are growing concerns for universities worldwide. Traditional security measures, such as firewalls and antivirus software, are often not sufficient to detect and prevent sophisticated attacks.
AI-based intrusion detection systems (IDS) provide an advanced solution by leveraging machine learning algorithms to monitor network traffic and identify unusual patterns that may indicate security breaches. These systems continuously learn from network behavior, improving their ability to detect new and evolving threats. By using AI for digital security, universities can strengthen their defenses, protect sensitive data, and respond to threats in real time.
Kebbi State University of Science and Technology, Aliero, like many other institutions, faces increasing cybersecurity risks as its digital infrastructure grows. The university’s reliance on online platforms for various administrative and academic functions exposes it to potential security breaches. This study explores the design, implementation, and effectiveness of an AI-based intrusion detection system for enhancing digital security at Kebbi State University.
Statement of the Problem
The current cybersecurity measures at Kebbi State University are insufficient to detect and prevent advanced digital security threats, such as data breaches and unauthorized access to student records and financial systems. Traditional security measures are reactive and may not effectively identify new or subtle forms of intrusion. The introduction of AI-based intrusion detection systems could provide a more proactive, adaptive, and accurate approach to university digital security. However, there is limited research on the feasibility and effectiveness of implementing AI-based security systems in Nigerian universities.
Objectives of the Study
- To design and implement an AI-based intrusion detection system to enhance digital security at Kebbi State University.
- To evaluate the effectiveness of the AI-based system in detecting and preventing cybersecurity threats at the university.
- To explore the challenges and opportunities of implementing AI-based intrusion detection systems in Nigerian universities.
Research Questions
- How effective is the AI-based intrusion detection system in identifying and preventing cybersecurity threats at Kebbi State University?
- What are the advantages of AI-based intrusion detection systems over traditional security measures in protecting university digital infrastructure?
- What challenges and opportunities exist in implementing AI-based digital security systems in Nigerian universities?
Research Hypotheses
- AI-based intrusion detection systems are more effective than traditional security measures in detecting cybersecurity threats at Kebbi State University.
- The implementation of AI-based intrusion detection systems reduces the occurrence of security breaches and unauthorized access to sensitive information.
- The adoption of AI-based intrusion detection systems faces challenges related to infrastructure readiness, staff training, and data privacy concerns.
Significance of the Study
This study will provide valuable insights into the use of AI for enhancing digital security in Nigerian universities. The findings will assist Kebbi State University and other institutions in improving their cybersecurity posture, protecting sensitive data, and reducing the risk of cyber threats.
Scope and Limitations of the Study
The study will focus on the design, implementation, and evaluation of an AI-based intrusion detection system at Kebbi State University of Science and Technology, Aliero. The scope will be limited to network security and the detection of unauthorized access and other security breaches.
Definitions of Terms
AI-Based Intrusion Detection System: A system that uses artificial intelligence algorithms to monitor network traffic and detect abnormal behavior that may indicate a security threat.
Cybersecurity Threats: Risks or attacks aimed at compromising the security of digital systems, including data breaches, hacking, and unauthorized access.
Machine Learning: A type of AI that enables systems to learn and improve their performance by analyzing data without explicit programming.
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