Abstract: Implementations are directed to methods for detecting and identifying advanced persistent threats (APTs) in networks, including receiving first domain activity data from a first network domain and second domain activity data from a second network domain, including multiple alerts from the respective first and second network domains and where each alert of the multiple alerts results from one or more detected events in the respective first or second network domains. A classification determined for each alert of the multiple alerts with respect to a cyber kill chain. A dependency is then determined for each of one or more pairs of alerts and a graphical visualization of the multiple alerts is generated, where the graphical visualization includes multiple nodes and edges between the nodes, each node corresponding to the cyber kill chain and representing at least one alert, and each edge representing a dependency between alerts.

Abstract: Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a network event graph based on at least one of a first graph metric or a second graph metric. The first and second graph metrics are based on the one or more extracted parameters. The data analysis device detects, based on queries performed on the network event graph, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Implementations are directed to methods for detecting and identifying advanced persistent threats (APTs) in networks, including receiving first domain activity data from a first network domain and second domain activity data from a second network domain, including multiple alerts from the respective first and second network domains and where each alert of the multiple alerts results from one or more detected events in the respective first or second network domains. A classification determined for each alert of the multiple alerts with respect to a cyber kill chain. A dependency is then determined for each of one or more pairs of alerts and a graphical visualization of the multiple alerts is generated, where the graphical visualization includes multiple nodes and edges between the nodes, each node corresponding to the cyber kill chain and representing at least one alert, and each edge representing a dependency between alerts.

Abstract: Implementations are directed to methods for detecting and identifying advanced persistent threats (APTs) in networks, including receiving first domain activity data from a first network domain and second domain activity data from a second network domain, including multiple alerts from the respective first and second network domains and where each alert of the multiple alerts results from one or more detected events in the respective first or second network domains. A classification determined for each alert of the multiple alerts with respect to a cyber kill chain. A dependency is then determined for each of one or more pairs of alerts and a graphical visualization of the multiple alerts is generated, where the graphical visualization includes multiple nodes and edges between the nodes, each node corresponding to the cyber kill chain and representing at least one alert, and each edge representing a dependency between alerts.

Abstract: Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a network event graph based on at least one of a first graph metric or a second graph metric. The first and second graph metrics are based on the one or more extracted parameters. The data analysis device detects, based on queries performed on the network event graph, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a network event graph based on at least one of a first graph metric or a second graph metric. The first and second graph metrics are based on the one or more extracted parameters. The data analysis device detects, based on queries performed on the network event graph, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Implementations are directed to methods for detecting and identifying advanced persistent threats (APTs) in networks, including receiving first domain activity data from a first network domain and second domain activity data from a second network domain, including multiple alerts from the respective first and second network domains and where each alert of the multiple alerts results from one or more detected events in the respective first or second network domains. A classification determined for each alert of the multiple alerts with respect to a cyber kill chain. A dependency is then determined for each of one or more pairs of alerts and a graphical visualization of the multiple alerts is generated, where the graphical visualization includes multiple nodes and edges between the nodes, each node corresponding to the cyber kill chain and representing at least one alert, and each edge representing a dependency between alerts.

Abstract: Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a network event graph based on at least one of a first graph metric or a second graph metric. The first and second graph metrics are based on the one or more extracted parameters. The data analysis device detects, based on queries performed on the network event graph, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining threat data contextualization.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining threat data contextualization.