Abstract: A machine learning model is scanned to detect actual or potential threats. The threats can be detected before execution of the machine learning model or during an isolated execution environment. The threat detection may include performing a machine learning file format check, vulnerability check, tamper check, and stenography check. The machine learning model may also be monitored in an isolated environment during an execution or runtime session. After performing a scan, the system can generate a signature based on actual, potential, or absence of detected threats.

Abstract: A system detects and responds to malicious acts directed towards machine learning models. Data fed into and output by a machine learning model is collected by a sensor. The data fed into the model includes vectorization data, which is generated from raw data provided from a requester, such as for example a stream of timeseries data. The output data may include a prediction or other output generated by the machine learning model in response to receiving the vectorization data. The vectorization data and machine learning model output data are processed to determine whether the machine learning model is being subject to a malicious act (e.g., attack). The output of the processing may indicate an attack score. A response for handling the request by a requester may be selected based on the output that includes the attack score, and the response may be applied to the requestor.

Abstract: A system detects and responds to malicious acts directed towards machine learning models. Data fed into and output by a machine learning model is collected by a sensor. The data fed into the model includes vectorization data, which is generated from raw data provided from a requester, such as for example a stream of timeseries data. The output data may include a prediction or other output generated by the machine learning model in response to receiving the vectorization data. The vectorization data and machine learning model output data are processed to determine whether the machine learning model is being subject to a malicious act (e.g., attack). The output of the processing may indicate an attack score. A response for handling the request by a requester may be selected based on the output that includes the attack score, and the response may be applied to the requestor.

Abstract: A flexible bushing for a cam follower, comprising a substantially rigid inner sleeve including an inner cam follower engaging surface, a length of the inner sleeve being greater than a thickness of a ram into which the flexible bushing is inserted, a substantially rigid outer sleeve including an outer ram engaging surface, a length of the outer sleeve being substantially equal to the thickness of the ram, the inner and outer sleeves being substantially concentric, and a shock absorbing flexible material disposed between the inner and outer sleeves.

Abstract: A flexible bushing for a cam follower, comprising a substantially rigid inner sleeve including an inner cam follower engaging surface, a length of the inner sleeve being greater than a thickness of a ram into which the flexible bushing is inserted, a substantially rigid outer sleeve including an outer ram engaging surface, a length of the outer sleeve being substantially equal to the thickness of the ram, the inner and outer sleeves being substantially concentric, and a shock absorbing flexible material disposed between the inner and outer sleeves.