Abstract: Mechanisms for protecting an application programming interface (API) are provided. The mechanisms include: receiving a combined API message containing sensor data from an API client and an API message; separating the sensor data and the API message; classifying the sensor data; determining that the API message is not to be blocked based on the classifying; and processing the API message. In some embodiments, the mechanisms further include preparing the sensor data for classification. In some embodiments, preparing the sensor data for classification comprises formatting the sensor data as an image. In some embodiments, classifying the sensor data comprises classifying the sensor data using a convolutional neural network (CNN). In some embodiments, the CNN is a ResNet CNN. In some embodiments, separating the sensor data and the API message comprises removing the sensor data from a header of the API message.

Abstract: A kit for determining ctDNA concentration and a method for determining ctDNA concentration is disclosed, wherein the method comprising dissolving extracted ctDNA in Tris-HCl buffer with pH>7.0 and adding into the buffer 2,7-bis(1-methyl-4-vinylpyridine)-9-ethylcarbazole iodised salt as a molecular probe, calculating the concentration of ctDNA in the solution by measuring the Fluorescence intensity of the solution. It has extremely high sensitivity in the determination of 0 ?g/ml˜50 ?g/ml ctDNA solution.

Abstract: A kit for determining ctDNA concentration and a method for determining ctDNA concentration is disclosed, wherein the method comprising dissolving extracted ctDNA in Tris-HCl buffer with pH>7.0 and adding into the buffer 2,7-bis(1-methyl-4-vinylpyridine)-9-ethylcarbazole iodised salt as a molecular probe, calculating the concentration of ctDNA in the solution by measuring the Fluorescence intensity of the solution. It has extremely high sensitivity in the determination of 0 ?g/ml˜50 ?g/ml ctDNA solution.

Abstract: A method, system and device for monitoring data. A server receives service running data of a service transmitted from a client. The server acquires a first abnormal strategy corresponding to the service and provides an alarm for the service when the first abnormal strategy is matched according to the service running data. The server can determine for which service running on the client the abnormality occurs and an alarm is provided.

Abstract: A method and device for identifying an abnormal application are provided. The method includes executing abnormal applications, obtaining dynamic behavior information of the abnormal applications, inputting the dynamic behavior information of the abnormal applications into a preset detection network, obtaining a behavior rule of the dynamic behavior information via the detection network, and identifying a detected application according to the behavior rule to determine whether the detected application is an abnormal application.

Abstract: A method, system and device for monitoring data. A server receives service running data of a service transmitted from a client. The server acquires a first abnormal strategy corresponding to the service and provides an alarm for the service when the first abnormal strategy is matched according to the service running data. The server can determine for which service running on the client the abnormality occurs and an alarm is provided.

Abstract: A method and device for identifying an abnormal application are provided. The method includes executing abnormal applications, obtaining dynamic behavior information of the abnormal applications, inputting the dynamic behavior information of the abnormal applications into a preset detection network, obtaining a behavior rule of the dynamic behavior information via the detection network, and identifying a detected application according to the behavior rule to determine whether the detected application is an abnormal application.