Predicting cybersecurity threats is an increasingly complex challenge in today’s digital lan‎dscape. Attacks constantly evolve an‎d adopt new forms, rendering traditional, reactive incident response methods insufficient. The core problem is that while organizations possess vast amounts of data on past cyberattacks an‎d security incidents, this data is often unstructured, fragmented, an‎d not systematically utilized to extract behavioral patterns that could help predict future attacks. This research aims to address this critical gap by designing an‎d developing a system based on Knowledge Management Systems (KMS) principles. This system will work to collect, classify, analyze, an‎d document lessons learned an‎d recurring patterns from past attacks in a centralized knowledge base. The seminar covers the fundamental concepts of KMS, methodologies for collecting an‎d analyzing threat data, an‎d extracting recurring patterns using Big Data analytics an‎d Machine Learning (ML), in addition to building a predictive model to identify potential attack types an‎d attack vectors. The topic’s significance stems from its direct impact on enhancing an organizationʹs cyber resilience, enabling proactive measures before an attack occurs, an‎d significantly reducing financial an‎d operational losses. This work establishes a crucial bridge between the fields of Knowledge Management an‎d Cyber Threat Intelligence.

2/18/2026 12:00:00 AM

89694

1404/11/29

Knowledge Management Systems for Cybersecurity Threat Prediction

Knowledge Management , Cybersecurity , Proactive Defense , Threat Prediction , Institutional Memory