3 results about "Program completion" patented technology

The application discloses a neural machine translation Internet of Things remote proof method for data flow attacks, which comprises an offline stage and a runtime verification stage. In the offline stage, a verifier and a prover first complete the negotiation of a symmetric key for subsequent remote proof, and at the same time, complete static plugging for a target program. A program control flow dataset is pre-constructed through fuzzy testing, a neural machine translation model is trained to establish the mapping of program input to an execution path, and a control flow graph is embedded to provide a structured prior for decoder attention. In the runtime verification stage, the verifier initiates a proof challenge to the prover, the prover provides the program input and the control flow path of the last execution, the verifier predicts a benign path from the program input by the neural machine translation model, and the difference between the benign predicted path and the actual path is used to judge the legitimacy of the prover. The application realizes accurate modeling of the program execution path, and shows effective detection capability for the abnormal path triggered by malicious input containing real vulnerabilities, and a good balance is achieved between detection coverage and running overhead.

The embodiment of the application belongs to the field of artificial intelligence, is applied to the field of cross-platform interaction, and relates to a cross-platform task execution method and device, computer equipment and a storage medium, which comprises the following steps: acquiring a system image of a first platform and caching the system image into a Docker engine; acquiring machine resources of a second platform, deploying the Docker engine, and mounting a preset NAS mounting volume into the deployed Docker engine; acquiring a pre-created AI processing task through the Docker engine, taking the system image as an execution program, taking the machine resources as a configuration program, and completing AI processing system construction; calling the constructed AI processing system to run the AI processing task, acquiring a task execution result, and storing the task execution result in the NAS mounting volume. The application realizes cross-platform task execution in a state where business logic and configuration resources are highly separated across platforms through a NAS mounting volume and a Docker engine, and avoids excessive coupling among multiple platforms.