Enhancing Network Intrusion Detection Systems with AI in Federal University, Dutsin-Ma, Katsina State
- Project Research
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- NGN 5000
Background of the Study
Intrusion Detection Systems (IDS) are essential for monitoring and identifying malicious activities on university networks. Federal University, Dutsin-Ma, Katsina State, supports a large and diverse network, which includes sensitive academic data, administrative systems, and personal information. Given the rapid growth in cyber threats such as Distributed Denial-of-Service (DDoS) attacks, ransomware, and phishing attempts, it is critical to implement an IDS that can effectively detect and respond to these threats.
While traditional IDS solutions rely on predefined signatures or rule-based systems, the complexity and sophistication of modern cyberattacks often render these approaches less effective. Artificial Intelligence (AI) offers a potential solution to this problem by enabling IDS to learn from network traffic patterns and adapt to new threats. This study aims to explore how AI can enhance the performance of IDS at Federal University, Dutsin-Ma, improving its ability to detect and respond to evolving cyber threats.
Statement of the Problem
Despite the implementation of IDS at Federal University, Dutsin-Ma, current systems may struggle to detect advanced and evolving cyberattacks. The reliance on traditional signature-based approaches limits the effectiveness of intrusion detection. This study will investigate how the integration of AI-driven techniques into the IDS can improve detection capabilities, reduce false positives, and enable quicker response times to network intrusions.
Objectives of the Study
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To evaluate the current IDS deployed at Federal University, Dutsin-Ma, Katsina State.
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To explore the role of AI in improving the detection accuracy and response time of IDS.
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To propose an AI-enhanced IDS model tailored to the network security needs of Federal University, Dutsin-Ma.
Research Questions
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How effective is the current IDS in detecting and preventing intrusions on the network at Federal University, Dutsin-Ma?
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In what ways can AI enhance the performance of IDS in terms of detection accuracy and response time?
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What AI-based techniques can be integrated into IDS to improve network security at Federal University, Dutsin-Ma?
Significance of the Study
This study will provide Federal University, Dutsin-Ma, with a more effective approach to securing its network infrastructure by leveraging AI to enhance the capabilities of its IDS. The improved system will offer better protection against cyber threats, reduce the risk of data breaches, and ensure that critical systems remain operational.
Scope and Limitations of the Study
The study will focus on enhancing the existing IDS with AI techniques for Federal University, Dutsin-Ma. It will not cover other aspects of network security such as firewall configuration or endpoint protection.
Definitions of Terms
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Intrusion Detection System (IDS): A system designed to detect and alert administrators to potential security breaches or malicious activity in a network.
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Artificial Intelligence (AI): The simulation of human intelligence in machines, allowing them to learn from data and make decisions autonomously.
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False Positives: Instances where a security system incorrectly identifies legitimate activity as a threat.
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