By 2025, the expected 50 billion connected devices worldwide will heighten risks of physical and direct attacks on critical systems. The Internet of Battlefield Things (IoBT) and embedded devices in industrial controls and infrastructure further amplify the threat. Traditional anti-tamper systems, limited by deterministic responses to specific attack types, are becoming inadequate due to advanced stealthier attacks. Intelligent defenses are essential for extended operational life and keeping pace with evolving threats. This study advocates for sophisticated anti-tamper solutions using machine learning to bolster physical security. The proposed system can detect various activities, discern normal operations from anomalies and known attacks, and has a stratified response and recovery mechanism, enhancing longevity and minimizing false positives. The growing IoBT usage necessitates better anti-tamper designs, and this research also explores the system’s resilience against adversarial learning attacks. We applied decision tree, SVM, and bagging classifiers and found that bagging classifier achieves 100% accuracy, outperforming the other used algorithms.