Embodiments of the present technology relate to cyber attack vulnerability analyses. In one embodiment, a method includes determining an external infrastructure of an entity, the external infrastructure including one or more cyber assets utilized by the entity, collecting infrastructure information regarding the one or more cyber assets, performing passive cyber security vulnerability testing on the one or more cyber assets using the collected infrastructure information, and assessing cyber security vulnerabilities of the one or more cyber assets. The method may further include calculating an association score for the one or more cyber assets based on the assessed cyber security vulnerabilities, and automatically recommending, based on the association score, computer network changes to reduce the cyber security vulnerabilities.

Cyber Vulnerability Scan Analyses with Actionable Feedback

Various embodiments of the present technology include methods of assessing risk of a cyber security failure in a computer network of an entity. Various embodiments also include automatically determining, based on the assessed risk, a change or a setting to at least one element of policy criteria of a cyber security policy, automatically recommending, based on the assessed risk, computer network changes to reduce the assessed risk, and providing one or more recommended computer network changes to reduce the assessed risk. Various embodiments further include enactment by the entity of at least one of the one or more of the recommended computer network changes to reduce the assessed risk to the entity, determining that the entity has enacted at least a portion of the recommended computer network changes, and in response, automatically reassessing the risk of a cyber security failure based on the enacted recommended computer network changes.

Inferential analysis using feedback for extracting and combining cyber risk information

Various embodiments of the present technology relate to diversity and similarity analysis. In some exemplary embodiments, a method includes, for each of a plurality of entities, receiving a set of variables that are indicative of attributes of an entity. The exemplary method also includes comparing the sets of variables for the plurality of entities to each other, locating clusters of similar variables shared between two or more of the plurality of entities, determining a probable maximum loss for the plurality of entities that share the clusters, the probable maximum loss being a loss value attributed to a cyber event against one or more of the shared variables, receiving feedback from an end user in response to providing the probable maximum loss to the end user, and updating the probable maximum loss in response to the feedback.

Diversity analysis with actionable feedback methodologies

Systems and methods for cyber risk analysis and remediation using network monitored sensors are provided herein. An example system includes one or more data collecting devices deployed within a network that collect entity information and monitor network traffic of the network that is related to security information. The network includes computing systems that are subject to a cyber risk policy having breach parameters defining one or more events that are indicative of a cyber security breach. A cyber security risk assessment and management system is used to automatically detect occurrence of one or more of the events that are indicative of a cyber security breach, automatically determine the breach parameters that apply for the one or more events that occurred, and generates a remediation of cyber security parameters for the network.

Cyber risk analysis and remediation using network monitored sensors and methods of use

Embodiments are directed towards managing data using modeling platform. A principal data set and match data set may be provided. The principal data set may include principal objects and the match data set may include other model objects. Blocking may associate each of the plurality of principal objects with potential match objects. Principal objects from the plurality of principal objects may be selected based on heuristics, their associated potential match objects, or the like. Potential match objects may be labeled as a true match or a non-match with respect to their associated principal object. A ranker may be trained to identify top-ranked matches based on the labeled potential match objects. The trained ranker may be employed to indicate matches where each matching other model object is a potential match object that is ranked higher than other potential match objects that are associated with its matched principal object.

Machine-assisted object matching

An apparatus includes a communications component to receive a specified variable and one or more specified criteria to select a final clustered representation of a network, the specified criteria including a maximum degree of loss of information for the specified variable for the final clustered representation; and an iterative collapse component to perform iteration(s) of deriving the final clustered representation. Each iteration includes calculating the degree of loss from each possible combination of two linked nodes of a current clustered representation to generate a next clustered representation; selecting the possible combination associated with a smallest degree of loss; determining whether to cease iterations based on whether the smallest degree associated with the selected combination exceeds the maximum degree; effecting the selected combination if the smallest degree doesn't exceed the maximum degree; and selecting the current clustered representation as the final clustered representation if the smallest degree exceeds the maximum degree.

Techniques for generating a clustered representation of a network based on node data

A method of determining a similarity between records in a data set is provided. Data organized into a plurality of records is received. First characters associated with a field and a first record of the plurality of records are selected. The selected first characters are subdivided into a first sliding series of a defined number of characters. Second characters associated with the field and a second record of the plurality of records are selected. The selected second characters are subdivided into a second sliding series of the defined number of characters. A similarity score between the first sliding series and the second sliding series is calculated. Whether or not the first sliding series and the second sliding series are similar is determined based on the calculated similarity score.

Similarity determination between anonymized data items

Various embodiments are generally directed to techniques for job flow processing, such as by ordering the performance of parallel tasks in a job flow to minimize a makespan for the job flow, for instance. Some embodiments are particularly directed to ordering the performance of tasks in a job flow based on computation of one or more independent and dependent metrics for tasks in a job flow. In many embodiments, tasks along a critical path of a job flow may be identified and prioritized using the one or more metrics computed for tasks in the job flow. For example, computing a time remaining until end and/or a longest path to end for each task in a job flow may enable a listing of tasks in the job flow to be ordered in a manner that prioritizes tasks to optimize the makespan for the job flow to be executed.

James Edward Georges

Papers

Techniques for generating a clustered representation of a network based on node data

Similarity determination between anonymized data items

Techniques for job flow processing