An Approach for Detecting and Preventing DDoS Attacks in Campus Networks

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks pose significant threats to network security, particularly in campus environments. This paper evaluates the performance and detection accuracy of Snort, an open-source intrusion detection system, in mitigating TCP/SYN Flooding DDoS attacks. We propose a novel approach to enhance Snort's capabilities by integrating new detection rules and assessing its impact on network resources, such as bandwidth, CPU load, and memory usage. Through comprehensive analysis of campus network vulnerabilities, we provide a robust security framework that addresses both existing and emerging threats. Our findings demonstrate the effectiveness of Snort in maintaining network integrity and highlight the potential for further enhancements in intrusion detection systems.