Detecting Cyber-Physical Attacks in the Smart Grid and the Internet of Things

Abstract

The increasing use of Smart Grid deployments and the Internet of Things (IoT) has brought attention to the risks and weaknesses related to cyber-physical attacks on critical infrastructure. The detection of cyber-physical dangers in the context of smart grids and the internet of things is thoroughly examined in this study. The study highlights the links between physical and cyber components while examining the particular difficulties presented by the integration of many technologies. Numerous attack vectors are analyzed, such as physical system modifications, communication network vulnerabilities, and malware transmission. The use of anomaly detection, intrusion detection systems, and machine learning techniques in detection methodologies is thoroughly examined and contrasted. Advanced analytics is incorporated into the proposed architecture to improve the resilience of techniques for identifying cyber-physical threats.The study also discusses reaction tactics, the necessity of reliable and adaptable systems, and the consequences of false positives. The findings help ongoing efforts to secure critical infrastructure by providing practitioners, researchers, and policymakers with valuable insights for protecting IoT ecosystems and Smart Grids against new and emerging cyber threats.