Optimization of Network Firewalls Using AI-Based Intrusion Detection in Federal University, Birnin Kebbi, Kebbi State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
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- NGN 5000
Background of the Study
Network firewalls are essential components of university network security, designed to monitor and control incoming and outgoing network traffic based on predetermined security rules. However, as cyberattacks grow in sophistication and scale, traditional firewalls may struggle to detect and prevent more advanced threats such as zero-day attacks or distributed denial-of-service (DDoS) attacks (Zhou et al., 2023). Integrating artificial intelligence (AI) with intrusion detection systems (IDS) can enhance the capability of firewalls, allowing them to automatically detect and mitigate these complex security threats.
Federal University, Birnin Kebbi, Kebbi State, relies on its network infrastructure to support academic and administrative functions. With an increasing volume of network traffic and the constant threat of cyberattacks, the university requires more intelligent solutions for identifying and preventing unauthorized access to its network. AI-based intrusion detection systems can be trained to recognize patterns of malicious activity and take proactive measures to mitigate potential risks. This study aims to explore the optimization of network firewalls at the university by incorporating AI-driven intrusion detection to enhance security and efficiency.
Statement of the Problem
Although Federal University, Birnin Kebbi, has implemented basic network firewalls to protect its infrastructure, these firewalls are not sufficient to defend against more advanced threats. The increasing complexity of cyberattacks requires smarter, AI-powered systems that can detect and respond to threats in real time. This study aims to address the limitations of current firewall technology by incorporating AI-based intrusion detection to improve the university’s network security.
Objectives of the Study
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To investigate the current use of network firewalls at Federal University, Birnin Kebbi, and identify security gaps.
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To design and implement an AI-based intrusion detection system that enhances the functionality of network firewalls.
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To evaluate the effectiveness of the AI-powered firewall system in detecting and mitigating network threats.
Research Questions
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How can AI-based intrusion detection enhance the effectiveness of traditional network firewalls at Federal University, Birnin Kebbi?
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What are the limitations of the current firewall system in protecting against advanced cyberattacks?
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How effective is the AI-powered firewall system in identifying and mitigating potential threats in real-time?
Significance of the Study
The implementation of AI-based intrusion detection systems will significantly strengthen Federal University, Birnin Kebbi’s network security by enabling more intelligent threat detection and faster response times. The study will help ensure that the university’s network is better protected against emerging threats and vulnerabilities.
Scope and Limitations of the Study
The study is focused on optimizing network firewalls at Federal University, Birnin Kebbi, with the inclusion of AI-based intrusion detection. It will not cover broader aspects of cybersecurity, such as endpoint security or cloud security, and will be limited to the university’s internal network.
Definitions of Terms
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Intrusion Detection System (IDS): A security tool used to detect unauthorized access or anomalies within a network.
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Artificial Intelligence (AI): The use of machine learning algorithms and advanced data analytics to simulate human-like decision-making.
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Firewall: A network security system that monitors and controls incoming and outgoing network traffic based on security rules.
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