The security and dependability of smart power grids are crucially dependent on intrusion detection. Smart grids' use of modern communication and technological advances opens up new security gaps and areas for attack. IDS (Intrusion Detection Systems) is created to watch over and examine network traffic to spot any suspicious or fraudulent activity that might point to unauthorized access or possible cyber threats. By using data pretreatment approaches such as SMOTE for dealing with imbalanced datasets, z-score or MinMax scaling for feature normalization and PCA for dimensionality reduction, this study suggests an intrusion detection framework for smart power grids. The Support Vector Machine (SVM) algorithm is used to perform the classification problem. The framework intends to improve the precision and effectiveness of intrusion detection in smart power grids while also offering a strong defense against online attacks and guaranteeing the infrastructure's safety and dependability.

Intrusion Detection, Classification, Threats, Attacks, System