Abstract: In one embodiment, an apparatus obtains unstructured text generated by a device regarding operation of the device. The apparatus identifies the unstructured text as associated with a particular command or process that generated the unstructured text. The apparatus classifies a portion of the unstructured text as anomalous by inputting the portion of the unstructured text to a machine learning-based model trained to predict text generated by the particular command or process. The apparatus provides provide the unstructured text for display that includes an indication that the portion of the unstructured text is anomalous.

Abstract: A method includes associating anomalous first text, from a first unstructured data set, with a first classification; processing the first unstructured data set using at least one of ML or AI to identify a second text that is in close context to the first text, and adding the second text to a text list associated with the first classification; enriching the text list by processing the second text to generate a third text, and adding the third text to the text list to produce an enriched text list and such that the third text is also associated with the first classification; matching the text in the enriched text list to text in a second unstructured data set; and classifying the text in the second unstructured data set as having the first classification when the text in the second unstructured data set matches text in the enriched text list.

Abstract: In one embodiment, a device obtains an output of a machine learning-based anomaly detector for unstructured text. The output of the anomaly detector includes a sequence of text analyzed by the detector and an indication that a portion of the sequence of text was flagged by the detector as an anomaly. The device extracts a context for the anomaly as an n-gram of portions of the sequence of text surrounding the anomaly. The device identifies a structure of the anomaly by identifying anchor portions of the extracted context. The device generates, based on the identified structure, an expression that represents the structure of the anomaly within the unstructured text.

Abstract: In one embodiment, an apparatus obtains unstructured text generated by a device regarding operation of the device. The apparatus identifies the unstructured text as associated with a particular command or process that generated the unstructured text. The apparatus classifies a portion of the unstructured text as anomalous by inputting the portion of the unstructured text to a machine learning-based model trained to predict text generated by the particular command or process. The apparatus provides provide the unstructured text for display that includes an indication that the portion of the unstructured text is anomalous.

Abstract: Systems, methods, and computer-readable media for distributing machine learning. In some examples, a first GAN model is deployed to a first network edge device and a second GAN model is deployed to a second network edge device. A generator of the first GAN model can be trained using real telemetry data of a first computing node and a generator of the second GAN model can be trained using real telemetry data of a second IoT device. The generator of the first GAN model and the generator of the second GAN model can be received. Additionally, a unified generator of a unified GAN model can be trained using the generator of the first GAN model and the generator of the second GAN model. Subsequently, the unified GAN model can be deployed to a third computing node for monitoring operation of the third IoT device.

Abstract: Systems, methods, and computer-readable media for distributing machine learning. In some examples, a first GAN model is deployed to a first network edge device and a second GAN model is deployed to a second network edge device. A generator of the first GAN model can be trained using real telemetry data of a first computing node and a generator of the second GAN model can be trained using real telemetry data of a second IoT device. The generator of the first GAN model and the generator of the second GAN model can be received. Additionally, a unified generator of a unified GAN model can be trained using the generator of the first GAN model and the generator of the second GAN model. Subsequently, the unified GAN model can be deployed to a third computing node for monitoring operation of the third IoT device.