Development of a Hybrid Intrusion Detection System for University Networks: A Case Study of Bingham University, Karu (Karu LGA, Nasarawa State)
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Background of the Study
With the increasing reliance on digital platforms for teaching, learning, and administrative tasks, university networks have become prime targets for cyberattacks. Bingham University, Karu, like many institutions, faces growing security concerns regarding the protection of sensitive data, such as student records, financial information, and research data. Intrusion Detection Systems (IDS) are critical for monitoring network traffic and detecting suspicious activities or potential security breaches. While traditional IDS methods focus on signature-based detection, modern systems also incorporate anomaly-based and hybrid detection techniques to enhance accuracy. This study explores the development of a hybrid intrusion detection system (IDS) that combines signature-based and anomaly-based methods to improve detection rates and minimize false alarms within the university network.
Statement of the Problem
Bingham University’s network is susceptible to various types of cyberattacks, including data breaches, denial of service, and malware infections. The existing intrusion detection system is primarily signature-based, which is effective for known threats but less effective for new or unknown attacks. As cyber threats evolve, there is a need for more sophisticated and adaptive IDS solutions. A hybrid IDS that combines the strengths of signature-based detection and anomaly detection could offer a more robust and effective defense against diverse threats. This study aims to design and implement such a system, addressing the limitations of traditional IDS solutions.
Objectives of the Study
- To develop a hybrid intrusion detection system that combines signature-based and anomaly-based techniques for Bingham University’s network security.
- To evaluate the performance of the hybrid IDS in detecting known and unknown cyberattacks compared to traditional signature-based systems.
- To reduce the rate of false positives and improve the accuracy of intrusion detection within the university network.
Research Questions
- How effective is a hybrid intrusion detection system in detecting both known and unknown threats in Bingham University’s network?
- What are the key advantages of combining signature-based and anomaly-based detection techniques in an IDS?
- How does the hybrid IDS compare to traditional signature-based IDS in terms of false positives and detection rates?
Research Hypotheses
- A hybrid IDS will outperform traditional signature-based systems in detecting both known and unknown cyber threats.
- The hybrid IDS will have a lower false positive rate compared to traditional signature-based IDS.
- The combination of signature-based and anomaly-based detection will significantly enhance the accuracy of intrusion detection within the university network.
Significance of the Study
This study will contribute to the improvement of network security at Bingham University, Karu, by developing a more robust and adaptive intrusion detection system. The research will help the university protect sensitive information, maintain the integrity of its systems, and ensure the safety of its network users.
Scope and Limitations of the Study
The study will focus on developing a hybrid IDS for Bingham University’s network (Karu LGA, Nasarawa State). The system will combine signature-based and anomaly-based detection methods to improve the accuracy of intrusion detection. Limitations include the challenge of obtaining comprehensive data on network traffic and the need for continuous updates to adapt to emerging cyber threats.
Definitions of Terms
Intrusion Detection System (IDS): A system designed to monitor network traffic and detect suspicious activities or potential security breaches.
Signature-Based Detection: An IDS method that relies on predefined signatures of known threats to identify malicious activities.
Anomaly-Based Detection: An IDS method that identifies deviations from normal network behavior, allowing it to detect unknown threats.
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