Abstract: A computing device trains a recurrent neural network (RNN), using a balanced dataset, to predict whether logs input to the RNN are indicative of respective successful computer code or respective failed computer code, the balanced dataset comprising positive log examples and negative log examples from a continuous integration (CI) pipeline, the positive log examples labelled as being indicative of successful computer code, and the negative log examples labelled as being indicative of failed computer code. The computing device inputs a log to the RNN, and monitors evolution of belief predictions of the RNN, as the RNN is analyzing the log, according to successive regions of the log. The computing devices determines, based on the evolution of the belief predictions, that a given region of the log meets a log fatal error criterion condition, and outputs an indication of the given region.

Abstract: Machine learning systems and methods for embedding attributed sequence data. The attributed sequence data includes an attribute data part having a fixed number of attribute data elements and a sequence data part having a variable number of sequence data elements. An attribute network module includes a feedforward neural network configured to convert the attribute data part to an encoded attribute vector having a first number of attribute features. A sequence network module includes a recurrent neural network configured to convert the sequence data part to an encoded sequence vector having a second number of sequence features. In use, the machine learning system learns and outputs a fixed-length feature representation of input attributed sequence data which encodes dependencies between different attribute data elements, dependencies between different sequence data elements, and dependencies between attribute data elements and sequence data elements within the attributed sequence data.

Abstract: Machine learning systems and methods for embedding attributed sequence data. The attributed sequence data includes an attribute data part having a fixed number of attribute data elements and a sequence data part having a variable number of sequence data elements. An attribute network module includes a feedforward neural network configured to convert the attribute data part to an encoded attribute vector having a first number of attribute features. A sequence network module includes a recurrent neural network configured to convert the sequence data part to an encoded sequence vector having a second number of sequence features. In use, the machine learning system learns and outputs a fixed-length feature representation of input attributed sequence data which encodes dependencies between different attribute data elements, dependencies between different sequence data elements, and dependencies between attribute data elements and sequence data elements within the attributed sequence data.

Abstract: Systems and methods for monitoring user authenticity during user activities in a user session on an application server is provided. The method being carried out in a distributed manner by a distributed server system. The method comprises a user modeling-process and a user-verification process. The user-modeling process is performed on a user-model server in which a user model is adapted session-by-session to user activity data received from the application server. The user-verification process is performed on the application server on the basis of the user model adapted on the user-model server. The user-verification process comprises comparing the user model with features extracted from user activity in the user session on the application server and determining a total risk-score value based on the comparison. If the total risk-score value is greater than a given threshold, a corrective action is performed.

Abstract: Systems and methods for monitoring user authenticity according to user activities on an application server. A user-modeling process and a user-verification process are performed. In the user-modeling process, a user model is adapted session-by-session to user activities in which the user model includes a plurality of adaptive feature-specific user-behavior models. The user-verification process includes determining a plurality of feature-specific risk-score values, comparing the at least one of the adaptive feature-specific user-behavior models with a respective feature extracted from user activity in the user session on the application server, and determining a total risk-score value indicative of user authenticity by weighting and combining the plurality of feature-specific risk-score values. If the total risk-score value is greater than a given threshold, a corrective action is performed.

Abstract: Systems and methods for monitoring user authenticity during user activities in a user session on an application server is provided. The method being carried out in a distributed manner by a distributed server system. The method comprises a user modeling-process and a user-verification process. The user-modeling process is performed on a user-model server in which a user model is adapted session-by-session to user activity data received from the application server. The user-verification process is performed on the application server on the basis of the user model adapted on the user-model server. The user-verification process comprises comparing the user model with features extracted from user activity in the user session on the application server and determining a total risk-score value based on the comparison. If the total risk-score value is greater than a given threshold, a corrective action is performed.

Abstract: Systems and methods for monitoring user authenticity according to user activities on an application server. A user-modeling process and a user-verification process are performed. In the user-modeling process, a user model is adapted session-by-session to user activities in which the user model includes a plurality of adaptive feature-specific user-behavior models. The user-verification process includes determining a plurality of feature-specific risk-score values, comparing the at least one of the adaptive feature-specific user-behavior models with a respective feature extracted from user activity in the user session on the application server, and determining a total risk-score value indicative of user authenticity by weighting and combining the plurality of feature-specific risk-score values. If the total risk-score value is greater than a given threshold, a corrective action is performed.