Abstract: According to examples, intelligent data munging may include ascertaining data that is to be transformed, and determining, based on an analysis of the ascertained data, a sample of the ascertained data. Intelligent data munging may further include enriching the sample of the ascertained data, determining features of the enriched sample of the ascertained data, and determining, based on the features, a transformation to be applied to the enriched sample of the ascertained data to transform the enriched sample of the ascertained data from a first format to a second format. Further, intelligent data munging may include validating the determined transformation, and generating, based on the validation of the determined transformation, a script that is to be applied to the ascertained data to transform the ascertained data from the first format to the second format.

Abstract: According to examples, network sampling based path decomposition and anomaly detection may include evaluating computer-generated log file data to generate a master network graph that specifies known events and transitions between the known events, and decomposing the master network graph to generate a representative network graph that includes a reduced number of paths of the master network graph. A source may be monitored to determine a cyber security threat by receiving incoming log file data related to the source, comparing the incoming log file data related to the source to the representative network graph, and determining, based on the comparison of the incoming log file data related to the source to the representative network graph, an anomaly in the representative network graph. Further, based on the monitoring, a report indicative of the cyber security threat may be generated based on the anomaly in the representative network graph.

Abstract: According to examples, network sampling based path decomposition and anomaly detection may include evaluating computer-generated log file data to generate a master network graph that specifies known events and transitions between the known events, and decomposing the master network graph to generate a representative network graph that includes a reduced number of paths of the master network graph. A source may be monitored to determine a cyber security threat by receiving incoming log file data related to the source, comparing the incoming log file data related to the source to the representative network graph, and determining, based on the comparison of the incoming log file data related to the source to the representative network graph, an anomaly in the representative network graph. Further, based on the monitoring, a report indicative of the cyber security threat may be generated based on the anomaly in the representative network graph.

Abstract: According to examples, intelligent data munging may include ascertaining data that is to be transformed, and determining, based on an analysis of the ascertained data, a sample of the ascertained data. Intelligent data munging may further include enriching the sample of the ascertained data, determining features of the enriched sample of the ascertained data, and determining, based on the features, a transformation to be applied to the enriched sample of the ascertained data to transform the enriched sample of the ascertained data from a first format to a second format. Further, intelligent data munging may include validating the determined transformation, and generating, based on the validation of the determined transformation, a script that is to be applied to the ascertained data to transform the ascertained data from the first format to the second format.