การวิเคราะห์และการตอบสนองต่อภัยคุกคามจากการโจมตีทางไซเบอร์ในระบบเครือข่ายขององค์กร

Amorn Juatee

Authors

Amorn Juatee Computer Education Program, Faculty of Science and Technology, Kanchanaburi Rajabhat University

Keywords:

Web Shell Backdoor, Malware, Password-Guessing Attack, Cybersecurity, Network

Abstract

This research aimed (1) to analyze the characteristics of cyberattacks occurring within the university network of Kanchanaburi Rajabhat University, (2) to examine the types, patterns, and impacts of threats on network devices, and (3) to improve security measures and develop response strategies for mitigating network-related threats. The study focused on analyzing malware attacks, web shell backdoors, and password-guessing attacks, along with investigating their patterns, impacts, and appropriate preventive measures. Data was collected through endpoint detection and response systems and a security event management system across 95 servers, with in-depth analysis conducted on 5 high-risk machines. The results revealed that malware was the most frequent threat, followed by web shell backdoors and password-guessing attacks, which, although less common, remained significant risks requiring continuous monitoring. The implementation of enhanced security measures — including web application firewalls, multi-factor authentication, and continuous software updates — significantly reduced threat incidents: malware decreased by 59.53%, web shell attacks by 60.61%, and password-guessing attacks by 33.33%. Furthermore, the study recommends integrating artificial intelligence (AI) and machine learning (ML) technologies to strengthen proactive threat detection, improve network security resilience, and sustainably enhance the organization’s capacity to respond to increasingly complex cyber threats.

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