Analyzing Vulnerabilities in Academic Network Servers: A Foundation for AI-Driven Intrusion Detection Systems
Server systems in academic settings have increasingly been the focus of cyber threats due in part to legacy configurations and weak authentication policies, and weak cybersecurity infrastructure. Through a systematic process of vulnerability scanning, configuration audits and log analysis across several academic institutions, their findings revealed significant vulnerabilities, particularly relating to legacy software, SQL injection vectors, and misconfigured firewalls. Their results indicate that 57% of vulnerabilities discovered were high risk (CVSS $\geq 8.0$) and that the two areas leading to the highest risk were weak access control and unpatched services. These findings provide a baseline for the subsequent implementation of an AI-based intrusion detection system that is tailored for the needs of academic institutions. Findings suggest that undertaking proactive vulnerability analysis is a critical first step prior to designing intelligent and adaptive network defence systems.
Paper
Full text
Analyzing Vulnerabilities in Academic Network Servers: A Foundation for AI-Driven Intrusion Detection Systems
Semantic Scholar · 2025
Abstract
Server systems in academic settings have increasingly been the focus of cyber threats due in part to legacy configurations and weak authentication policies, and weak cybersecurity infrastructure. Through a systematic process of vulnerability scanning, configuration audits and log analysis across several academic institutions, their findings revealed significant vulnerabilities, particularly relating to legacy software, SQL injection vectors, and misconfigured firewalls. Their results indicate that 57% of vulnerabilities discovered were high risk (CVSS $\geq 8.0$) and that the two areas leading to the highest risk were weak access control and unpatched services. These findings provide a baseline for the subsequent implementation of an AI-based intrusion detection system that is tailored for the needs of academic institutions. Findings suggest that undertaking proactive vulnerability analysis is a critical first step prior to designing intelligent and adaptive network defence systems.