Date Driven Security Threat Detection in IIOT Using Random Forest

Abstract

Security threats found within IIoT systems require specialized protection measures to safeguard essential infrastructure for dealing with essential issues. The proposed solution delivers a framework that effectively deals with Cyber Threat Intelligence generation for IIoT deployment contexts. The system recognizes cyber threats by using Random Forest algorithm together with artificial intelligence methods to establish proactive threat prevention that is both effective and automatic. SMOTE serves as an approach for handling unbalanced data in IIoT anomaly detection operations to create more consistent results. Smart Sentry implements analytical procedures throughout its proactive system design for security threat scanning of IIoT activities to ensure operation continuity prior to disruptions.