An Ensamble Model for Detecting Intranet Threats and Possible Attacks

Abstract- Detecting intranet attacks is tough because attackers keep changing their methods. This paper suggests a new way to catch these attacks using machine learning. The idea is to study how these attacks behave and use that knowledge to spot them early. By looking at things like network traffic and system logs, the system learns what’s normal and what’s not. This helps it flag any strange activity before it becomes a serious threat. The approach aims to boost intranet security by offering real-time detection and flexible ways to defend against attacks.