Abstract: Systems, devices, and methods for protecting a user computer devices/network from malicious code embedded in a neural network is described. A security platform may selectively modify a downloaded neural network model and/or architecture to remove neural network parameters that may be used to reconstruct the malicious code at an end user of the neural network model. For example, the security platform may remove specific branches of the neural network and/or set specific parameters of the neural network model to zero, such that the malicious code may not be reconstructed at an end-user device.

Abstract: Systems, devices, and methods for protecting a user computer devices/network from malicious code embedded in a neural network is described. A security platform may selectively modify a downloaded neural network model and/or architecture to remove neural network parameters that may be used to reconstruct the malicious code at an end user of the neural network model. For example, the security platform may remove specific branches of the neural network and/or set specific parameters of the neural network model to zero, such that the malicious code may not be reconstructed at an end-user device.

Abstract: Denoising of technology logs and presentation of resulting user interfaces that display actions/log entries in ranked order of importance. Specifically, technology logs are received for a plurality of entities (e.g., users, devices, systems or the like). In response to receiving the technology logs, a rarity indicator is determined for the various recorded actions/entries in all of the received logs based on the number of occurrences in all of the received logs. Subsequently, an importance indicator is determined for each recorded action in a corresponding technology log based on the rarity indicator and the number of occurrences of the recorded action in the corresponding technology log. In response to determining the importance indicator, user interface(s) are generated and displayed that provide for the actions to be displayed in ranked order of their importance indicators.

Abstract: A need to retrain a machine learning model is determined based on identification of either a new scenario (i.e., new grouping/population of data variables) or a modification to an existing/trained scenario (i.e., addition and/or deletion of data variables in an already trained scenario). Change occurring across all of the data variables used in training the machine learning model are monitored as opposed to monitoring only the data variables on an individual basis. When concurrent change occurs across multiple inter-related data variables, the need to add or modify a scenario (i.e., retrain a machine learning model) is identified.