Enterprise Threat Detection

Abstract

Enterprise environments are increasingly exposed to sophisticated cyber threats such as ransomware, phishing attacks, insider threats, Advanced Persistent Threats (APTs), and zero-day vulnerabilities. Conventional signature-based security mechanisms are often inadequate for identifying newly emerging or previously unknown attacks in real time. To address these challenges, this paper proposes an intelligent enterprise threat detection framework that combines machine learning techniques with Security Information and Event Management (SIEM), User and Entity Behavior Analytics (UEBA), and cyber threat intelligence. By correlating data from multiple security sources and analyzing behavioral patterns, the proposed framework enhances threat detection capabilities and improves classification accuracy. Experimental results indicate that the system effectively minimizes false-positive alerts, accelerates threat identification, and strengthens the overall cybersecurity posture of enterprise networks.