Program attacks are one of the oldest and fundamental cyber threats. They compromise the confidentiality of data, the integrity of program logic, and the availability of services. This threat becomes even severer when followed by other malicious activities such as data exfiltration. The integration of primitive attacks constructs comprehensive attack vectors and forms advanced persistent threats.

Along with the rapid development of defense mechanisms, program attacks and data leak threats survive and evolve. Stealthy program attacks can hide in long execution paths to avoid being detected. Sensitive data transformations weaken existing leak detection mechanisms. New adversaries, e.g., semi-honest service provider, emerge and form threats.

This thesis presents theoretical analysis and practical detection mechanisms against stealthy program attacks and data leaks. The thesis presents a unified framework for understanding different branches of program anomaly detection and sheds light on possible future program anomaly detection directions. The thesis investigates modern stealthy program attacks hidden in long program executions and develops a program anomaly detection approach with data mining techniques to reveal the attacks. The thesis advances network-based data leak detection mechanisms by relaxing strong requirements in existing methods. The thesis presents practical solutions to outsource data leak detection procedures to semi-honest third parties and identify noisy or transformed data leaks in network traffic.