THE DESIGN AND IMPLEMENTATION OF A SECURED NETWORK INTRUSION DETECTION SYSTEM USING SNORT (A CASE STUDY OF DEPARTMENT OF NETWORKING AND CLOUD COMPUTING)

Network security is foundational to modern IT infrastructure, necessitating robust safeguards like Intrusion Detection Systems (IDS) against increasingly sophisticated cyber threats. This study focused on the design, implementation, and evaluation of a secured Network Intrusion Detection System (NIDS) utilizing the open-source tool, Snort, within the heterogeneous network environment of the Department of Networking and Cloud Computing. The investigation addressed critical issues such as under-tuned rule sets, performance bottlenecks during high traffic, lack of automated alert correlation, and limited staff expertise prevalent in academic settings. An iterative methodology was adopted, encompassing network assessment, design (Snort on a Windows platform using Npcap), implementation, and testing through simulated attack scenarios. The project successfully demonstrated that a tailored Snort deployment is a cost-effective, flexible, and customizable solution. Customized rule sets were implemented, which significantly reduced false positive alerts while maintaining high detection coverage for relevant academic threats. Performance evaluations affirmed the system’s capability to handle peak network loads efficiently. The findings contribute a replicable IDS implementation framework that enhances the department's real-time threat detection capability, streamlines incident response through alert management, and aligns with institutional resource constraints, thereby strengthening the overall network security posture.