Vulnerability Assessment of Asphalt Plant through Machine Learning Techniques

Many businesses throughout the globe have recently realized value of Supervisory Control and Data Acquisition (SCADA) systems. critical infrastructures, such as electricity grids, asphalt plants, wastewater disposals, are controlled by these With introduction Fourth Industrial Revolution, 4IR or Industry 4.0, today’s SCADA systems cannot be separated from outside world, making them more susceptible to hostile assaults. Conventional security including different antivirus software firewalls unable safeguard they distinct requirements. For this, machine learning algorithms, i.e., SVM, KNN, random forest, tested cover anomaly detection along with protection for The dataset used in this research study was made locally an plant using sensor data grouped two classes: one is natural signal values, other attack class which values found out range while operation. Amongst above-mentioned KNN outperformed accuracy rate 89% any kind external can detected notified control room on-time actions.