Abstract: A computing device processor may be configured with processor-executable instructions to implement methods of detecting and responding to fake user interaction (UI) events. The processor may determine whether a user interaction event is a fake user interaction event by analyzing raw data generated by one or more hardware drivers in conjunction with user interaction event information generated or received by the high-level operating system. In addition, the processor may be configured with processor-executable instructions to implement methods of using behavioral analysis and machine learning techniques to identify, prevent, correct, or otherwise respond to malicious or performance-degrading behaviors of the computing device based on whether a detected user interaction event is an authentic or fake user interaction event.

Abstract: Methods and devices for tracking data flows in a computing device include monitoring memory in a hardware component of the computing device to identify a read operation that reads information from a tainted memory address, using heuristics to identify a first, second, and third number of operations performed after the identified read operation, marking memory addresses of write operations performed after first number of operations and before the second number of operations as tainted, and marking memory addresses of write operations performed after the third number of operations and before the second number of operations as untainted.

Abstract: A computing device processor may be configured with processor-executable instructions to implement methods of detecting and responding to fake user interaction (UI) events. The processor may determine whether a user interaction event is a fake user interaction event by analyzing raw data generated by one or more hardware drivers in conjunction with user interaction event information generated or received by the high-level operating system. In addition, the processor may be configured with processor-executable instructions to implement methods of using behavioral analysis and machine learning techniques to identify, prevent, correct, or otherwise respond to malicious or performance-degrading behaviors of the computing device based on whether a detected user interaction event is an authentic or fake user interaction event.

Abstract: Methods and devices for tracking data flows in a computing device include monitoring memory in a hardware component of the computing device to identify a read operation that reads information from a tainted memory address, using heuristics to identify a first, second, and third number of operations performed after the identified read operation, marking memory addresses of write operations performed after first number of operations and before the second number of operations as tainted, and marking memory addresses of write operations performed after the third number of operations and before the second number of operations as untainted.