Active Gait System for Real-Time Surveillance Against Cyber-Physical Attacks

Abstract

Cyberterrorism, espionage, and warfare are serious threats to national security. These attacks can harm people or destroy critical infrastructures like the data centres, computer networks, and systems. Surveillance systems currently used in monitoring critical infrastructures, national key points, and military exclusion zones (MEZ) are ineffective in detecting unauthorised intrusions. These issues compromise the stability of the countries, and the safety of the citizens and result in the loss of important assets. This experimental research study developed a Cyber Physical Security (CPS) defense gait-recognition monitoring system. Autonomous Machine Learning (ML) technology was employed to enhance the precision and reliability of the system against CPA, in tracking access, managing security clearances, and triggering alerts in the event of unauthorized entries to restricted areas.