Abstract: Systems and methods are disclosed to implement a cyberattack detection system that monitors a computer network for lateral movement. In embodiments, the system uses network data from a computer network to build a baseline of connection behaviors for the network. Connection graphs are generated from new network data that indicate groups of nodes that made connections with one another during a last time interval. The graphs are analyzed for connection behavior anomalies and ranked to determine a subset of graphs with suspected lateral movement. Graphs with suspected lateral movement may be further analyzed to determine a set of possible attack paths in the lateral movements. The suspected attack paths are reported to network administrators via a notification interface. Advantageously, the disclosed system is able to detect potential lateral movements in localized portions of a network by monitoring for connection behavior anomalies in network data gathered from the network.

Abstract: Various embodiments include systems and methods to implement a password requirement conformity check. During a password reset process, a proposed password is received. A homomorphic encryption operation may be performed on the proposed password to generate a first character string. The first character string may be compared to a previous character string associated with a previous password to determine a password similarity metric. The password similarity metric may or may not satisfy at least a distance threshold. Responsive to determining that the password similarity metric does not satisfy the distance threshold, there may be a rejection of the proposed password and a prompt to receive an alternative proposed password during the password reset process.

Abstract: An anomaly detection system is disclosed capable of reporting anomalous processes or hosts in a computer network using machine learning models trained using unsupervised training techniques. In embodiments, the system assigns observed processes to a set of process categories based on the file system path of the program executed by the process. The system extracts a feature vector for each process or host from the observation records and applies the machine learning models to the feature vectors to determine an outlier metric each process or host. The processes or hosts with the highest outlier metrics are reported as detected anomalies to be further examined by security analysts. In embodiments, the machine learnings models may be periodically retrained based on new observation records using unsupervised machine learning techniques. Accordingly, the system allows the models to learn from newly observed data without requiring the new data to be manually labeled by humans.

Abstract: Approaches provide for securing an electronic environment. A threat analysis service can obtain data for devices, users, and threats from disparate sources and can correlate users to devices and threats to build an understanding of an electronic environment's operational, organizational, and security concerns in order to provide customized security strategies and remediations. Additionally, the threat analysis service can develop a model of an electronic environment's behavior by monitoring and analyzing various the data from the data sources. The model can be updated such that the threat analysis service can tailor its orchestration to complement existing operational processes.

Abstract: Systems and methods are disclosed to implement a cyberattack detection system that monitors a computer network for lateral movement. In embodiments, the system uses network data from a computer network to build a baseline of connection behaviors for the network. Connection graphs are generated from new network data that indicate groups of nodes that made connections with one another during a last time interval. The graphs are analyzed for connection behavior anomalies and ranked to determine a subset of graphs with suspected lateral movement. Graphs with suspected lateral movement may be further analyzed to determine a set of possible attack paths in the lateral movements. The suspected attack paths are reported to network administrators via a notification interface. Advantageously, the disclosed system is able to detect potential lateral movements in localized portions of a network by monitoring for connection behavior anomalies in network data gathered from the network.

Abstract: Systems and methods are disclosed to implement a cyberattack detection system that monitors a computer network for lateral movement. In embodiments, the system uses network data from a computer network to build a baseline of connection behaviors for the network. Connection graphs are generated from new network data that indicate groups of nodes that made connections with one another during a last time interval. The graphs are analyzed for connection behavior anomalies and ranked to determine a subset of graphs with suspected lateral movement. Graphs with suspected lateral movement may be further analyzed to determine a set of possible attack paths in the lateral movements. The suspected attack paths are reported to network administrators via a notification interface. Advantageously, the disclosed system is able to detect potential lateral movements in localized portions of a network by monitoring for connection behavior anomalies in network data gathered from the network.

Abstract: Disclosed herein are methods, systems, and processes to detect valid clusters and eliminate spurious clusters in cybersecurity-based computing environments. A cluster detection and elimination model is trained by accessing a dataset with raw data that includes data points associated with computing devices in a network and applying two or more different clustering methodologies independently to the dataset. The resulting cluster detection and elimination model is used to compare two or more clusters to determine whether a cluster from one clustering methodology matches another cluster from another clustering methodology based on centroid locations and shared data points.

Abstract: Disclosed herein are methods, systems, and processes to optimize role level identification for computing resource allocation to perform security operations in networked computing environments. A role level classifier to process a training dataset that corresponds to a clean title is generated from a subset of entities associated with the clean title. An initial effective title determined by the role level classifier based on processing the training dataset is assigned to an entity. A new effective title based on feature differences between the initial effective title and the clean title is re-assigned to the entity. Performance of the generating, the assigning, and the re-assigning is repeated using the new effective title instead of the clean title.

Abstract: Analyzing and reporting anomalous internet traffic data by accepting a request for a connection to a virtual security appliance, collecting attribute data about the connection, applying an alert module to the data, and automatically generating an alert concerning an identified incident. An alert system for analyzing and reporting the anomalous internet traffic data. A processor to analyze and report anomalous internet traffic data.

Abstract: Disclosed herein are methods, systems, and processes to automate cluster interpretation in computing environments to develop targeted remediation security actions. To interpret clusters that are generated by a clustering methodology without subjecting clustered data to classifier-based processing, separation quantifiers that indicate a spread in feature values across clusters are determined and used to discover relative feature importances of features that drive the formation of clusters, permitting a security server to identify features that discriminate between clusters.

Abstract: Methods and systems for event prediction. The system includes at least one connector configured to gather at least partially incomplete data from at least one data source. The gathered data may be communicated to a model definition module that converts at least a subset of the gathered data into a prediction model in accordance with a received definition. A prediction module may receive a prediction query and, in response, supply an event prediction based on the prediction model.

Abstract: An anomaly detection system is disclosed capable of reporting anomalous processes or hosts in a computer network using machine learning models trained using unsupervised training techniques. In embodiments, the system assigns observed processes to a set of process categories based on the file system path of the program executed by the process. The system extracts a feature vector for each process or host from the observation records and applies the machine learning models to the feature vectors to determine an outlier metric each process or host. The processes or hosts with the highest outlier metrics are reported as detected anomalies to be further examined by security analysts. In embodiments, the machine learnings models may be periodically retrained based on new observation records using unsupervised machine learning techniques. Accordingly, the system allows the models to learn from newly observed data without requiring the new data to be manually labeled by humans.

Abstract: A system includes at least one connector configured to gather at least partially incomplete data from at least one data source. The gathered data is communicated to a model definition module that converts at least a subset of the gathered data into a prediction model in accordance with a received definition. A prediction module receives a prediction query and, in response, supplies an event prediction based on the prediction model.

Abstract: Analyzing and reporting anomalous internet traffic data by accepting a request for a connection to a virtual security appliance, collecting attribute data about the connection, applying an alert module to the data, and automatically generating an alert concerning an identified incident. An alert system for analyzing and reporting the anomalous internet traffic data. A processor to analyze and report anomalous internet traffic data.

Abstract: Disclosed herein are methods, systems, and processes to optimize role level identification for computing resource allocation to perform security operations in networked computing environments. A role level classifier to process a training dataset that corresponds to a clean title is generated from a subset of entities associated with the clean title. An initial effective title determined by the role level classifier based on processing the training dataset is assigned to an entity. A new effective title based on feature differences between the initial effective title and the clean title is re-assigned to the entity. Performance of the generating, the assigning, and the re-assigning is repeated using the new effective title instead of the clean title.

Abstract: Analyzing and reporting anomalous internet traffic data by accepting a request for a connection to a virtual security appliance, collecting attribute data about the connection, applying an alert module to the data, and automatically generating an alert concerning an identified incident. An alert system for analyzing and reporting the anomalous internet traffic data. A processor to analyze and report anomalous internet traffic data.

Abstract: Approaches provide for securing an electronic environment. A threat analysis service can obtain data for devices, users, and threats from disparate sources and can correlate users to devices and threats to build an understanding of an electronic environment's operational, organizational, and security concerns in order to provide customized security strategies and remediations. Additionally, the threat analysis service can develop a model of an electronic environment's behavior by monitoring and analyzing various the data from the data sources. The model can be updated such that the threat analysis service can tailor its orchestration to complement existing operational processes.

Abstract: Methods, systems, and processes to optimize role level identification for computing resource allocation to perform security operations in networked computing environments. A role level classifier to process a training dataset that corresponds to a clean title is generated from a subset of entities associated with the clean title. An initial effective title determined by the role level classifier based on processing the training dataset is assigned to an entity. A new effective title based on feature differences between the initial effective title and the clean title is re-assigned to the entity. Performance of the generating, the assigning, and the re-assigning is repeated using the new effective title instead of the clean title.

Abstract: Approaches provide for securing an electronic environment. A threat analysis service can obtain data for devices, users, and threats from disparate sources and can correlate users to devices and threats to build an understanding of an electronic environment's operational, organizational, and security concerns in order to provide customized security strategies and remediations. Additionally, the threat analysis service can develop a model of an electronic environment's behavior by monitoring and analyzing various the data from the data sources. The model can be updated such that the threat analysis service can tailor its orchestration to complement existing operational processes.

Abstract: Disclosed herein are methods, systems, and processes to detect valid clusters and eliminate spurious clusters in cybersecurity-based computing environments. A cluster detection and elimination model is trained by accessing a dataset with raw data that includes data points associated with computing devices in a network and applying two or more different clustering methodologies independently to the dataset. The resulting cluster detection and elimination model is used to compare two or more clusters to determine whether a cluster from one clustering methodology matches another cluster from another clustering methodology based on centroid locations and shared data points.