Exploring the Role of AI in Network Incident Response in Federal University, Wukari, Taraba State
- Project Research
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- Table of Content: Available
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- NGN 5000
Background of the Study
Network security incidents, such as data breaches, denial-of-service attacks, and malware infections, are an ongoing threat to educational institutions worldwide. Federal University, Wukari, Taraba State, faces these challenges as part of its daily operations. The timely identification, analysis, and response to network incidents are crucial for minimizing damage and ensuring the continuity of university operations. Traditional incident response methods, which often rely on manual processes and human expertise, can be slow and error-prone, especially when dealing with large volumes of network traffic and security data.
Artificial intelligence (AI) has shown significant potential in enhancing network incident response by automating threat detection, providing real-time analysis, and enabling quicker decision-making (Jiang et al., 2024). AI algorithms can analyze large amounts of network data to identify unusual patterns or behaviors that might indicate a security incident. This study will explore the role of AI in enhancing the university’s network incident response capabilities, focusing on its potential to improve detection, analysis, and mitigation processes.
Statement of the Problem
Federal University, Wukari faces challenges in responding to network security incidents in a timely and effective manner due to the increasing complexity of cyber-attacks. The reliance on manual methods for identifying and mitigating threats is slow and may lead to delayed responses, allowing attackers to cause more harm. This study aims to investigate the role of AI in automating and improving network incident response at the university, reducing response time, and enhancing overall network security.
Objectives of the Study
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To assess the current network incident response processes at Federal University, Wukari.
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To explore how AI technologies can be integrated into the university’s network security framework to improve incident detection and response.
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To evaluate the effectiveness of AI-powered incident response in reducing the impact of network security incidents.
Research Questions
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What are the current network incident response practices at Federal University, Wukari?
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How can AI improve the detection and response to network security incidents at the university?
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What are the benefits and challenges of implementing AI in network incident response at Federal University, Wukari?
Significance of the Study
This study will contribute to the understanding of how AI can enhance network incident response at Federal University, Wukari. By incorporating AI into the university’s security strategy, the university can respond to incidents more quickly and effectively, reducing the potential damage caused by cyber-attacks and improving overall network resilience.
Scope and Limitations of the Study
This study focuses on exploring the role of AI in network incident response within the context of Federal University, Wukari. It will evaluate existing network response strategies and propose AI-based solutions. However, it will not extend to other types of network security enhancements or broader organizational changes. The effectiveness of AI in response to specific types of incidents will also be considered.
Definitions of Terms
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Network Incident Response: The process of detecting, analyzing, and mitigating network security incidents to reduce their impact.
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Artificial Intelligence (AI): The simulation of human intelligence processes by machines, particularly computer systems.
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Threat Detection: The process of identifying potential security threats within a network through the analysis of data and system behaviors.
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