  The security trade confidentiality, integrity and availability are the main
pillar of the information systems as every organization emphasize of the
security. From last few decades, digital data is the main asset for every
digital or non-digital organization. The proliferation of easily accessible
attack software on the internet has lowered the barrier for individuals without
hacking skills to engage in malicious activities. An Industrial organization
operates a server that (Confluence) serves as a learning platform for newly
hired employees or Management training officers, thereby making it vulnerable
to potential attacks using readily available internet-based software. To
mitigate this risk, it is essential to implement a security system capable of
detecting and preventing attacks, as well as conducting investigations. This
research project aims to develop a comprehensive security system that can
detect attack attempts, initiate preventive measures, and carry out
investigations by analyzing attack logs. The study adopted a survey methodology
and spanned a period of four months, from March 1, 2023, to June 31, 2023. The
outcome of this research is a robust security system that effectively
identifies attack attempts, blocks the attacker's IP address, and employs
network forensic techniques for investigation purposes. The findings indicate
that deploying Snort in IPS mode on PfSense enables the detection of attacks
targeting e-learning servers, triggering automatic preventive measures such as
IP address blocking. The alerts generated by Snort facilitate investigative
actions through network forensics, allowing for accurate reporting on the
detrimental effects of the attacks.
