Patent Description:
The following presents a simplified summary of one or more implementations in order to provide a basic understanding of such implementations. This summary is not an extensive overview of all contemplated implementations, and is intended to neither identify key or critical elements of all implementations nor delineate the scope of any or all implementations. Its sole purpose is to present some concepts of one or more implementations in a simplified form as a prelude to the more detailed description that is presented later.

In an example, a computer implemented method for curating threat intelligence data is provided. The method includes receiving from multiple data sources threat intelligence data comprising a list of a first type of entities, a list of a second type of entities, an indication of an association between at least one of the first type of entities and at least one of the second type of entities, and a reputation score for each entity in the list of the first type of entities and the list of the second type of entities, the reputation score indicating a relative threat level of that entity in posing a security threat, wherein the first type of entity is an internet protocol, IP, address and wherein the second type of entity is a file has. The method also includes receiving a confidence value corresponding to the association, determining an updated reputation score for the at least one of the first type of entity based on the confidence value and the reputation score of the at least one of the second type of entity to which the at least one of the first type of entities is indicated to be associated, and updating, in the threat intelligence data, the reputation score of the at least one of the first type of entities to the updated reputation score. The confidence value indicates reliability of the association. The method also includes training, based on the threat intelligence data comprising the updated reputation score, a machine learning model to identify possible threats based on detecting trends in associations among the entities indicated by the threat intelligence data.

In another example, a device for curating threat intelligence data is provided. The device includes a memory for storing threat intelligence data and one or more parameters or instructions for curating the threat intelligence data, and at least one processor coupled to the memory. The at least one processor is configured to receive from multiple data sources the threat intelligence data comprising a list of a first type of entities, a list of a second type of entities, an indication of an association between at least one of the first type of entities and at least one of the second type of entities, and a reputation score for each entity in the list of the first type of entities and the list of the second type of entities, the reputation score indicating a relative threat level of that entity in posing a security threat, wherein the first type of entity is an internet protocol, IP, address and wherein the second type of entity is a file hash. The at least one processor is also configured to receive a confidence value corresponding to the association, determine an updated reputation score for the at least one of the first type of entity based on the confidence value and the reputation score of the at least one of the second type of entity to which the at least one of the first type of entities is indicated to be associated, and update, in the threat intelligence data, the reputation score of the at least one of the first type of entities to the updated reputation score. The confidence value indicates reliability of the association. The at least one processor is also configured to train, based on the threat intelligence data comprising the updated reputation score, a machine learning model to identify possible threats based on detecting trends in associations among entities indicated in the threat intelligence data.

In another example, a computer-readable medium, including code executable by one or more processors for curating threat intelligence data is provided. The code includes code for receiving from multiple data sources threat intelligence data comprising a list of a first type of entities, a list of a second type of entities, an indication of an association between at least one of the first type of entities and at least one of the second type of entities, and a reputation score for each entity in the list of the first type of entities and the list of the second type of entities, the reputation score indicating a relative threat level of that entity in posing a security threat, wherein the first type of entity is an internet protocol, IP, address and wherein the second type of entity is a file hash. The code also includes code for receiving a confidence value corresponding to the association, determining an updated reputation score for the at least one of the first type of entity based on the confidence value and the reputation score of the at least one of the second type of entity to which the at least one of the first type of entities is indicated to be associated, and updating, in the threat intelligence data, the reputation score of the at least one of the first type of entities to the updated reputation score. The confidence value indicates reliability of the association. The code also includes code for training, based on the threat intelligence data comprising the updated reputation score, a machine learning model to identify possible threats based on detecting trends in associations among entities indicated in the threat intelligence data.

To the accomplishment of the foregoing and related ends, the one or more implementations comprise the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative features of the one or more implementations. These features are indicative, however, of but a few of the various ways in which the principles of various implementations may be employed, and this description is intended to include all such implementations and their equivalents.

In some instances, well-known components are shown in block diagram form in order to avoid obscuring such concepts.

This disclosure describes various examples related to curating threat intelligence data from various sources. For example, the amount of threat intelligence data available from different sources (e.g., threat intelligence detection software vendors, blog posts on the Internet, etc.) can be vast, and there is currently no mechanism to correlate or maintain data from the various sources. For example, given threat intelligence data from multiple sources, certain parameters related to entities identified as posing a possible threat may be proprietary to the sources. In addition, similar threat intelligence data from the various sources may be associated with different expiration times. This may lead to expired threat intelligence data being incorrectly consumed and erroneous reputation scores for entities may be generated based on the expired data. In addition, such inconsistencies in the data may convolute analysis of the data by a data analyst and/or may generate false positives in a machine-learning algorithm applied for determining trends in the threat intelligence data.

In this regard, for example, aspects described herein improve interactions with threat intelligence data at least in part by allowing one or more parameters of the threat intelligence data to be updated to a normalized value to allow for improved association of the threat intelligence data. In one example, entities posing a possible threat can be identified from the threat intelligence data, where the entities can be of different types, including files (e.g., identified by file hash), computers (e.g., identified by network address, such as internet protocol (IP) address), or other entities. Entities of different types can be associated with one another and the association can be used to determine a reputation score for one or more of the entities. In curating the data, for example, where a computer communicates certain files, the files may be associated with the computer, and a reputation score for the computer can be updated based on a reputation score for one or more of the files. Similarly, for example, a reputation score for one or more of the files can be updated based on a reputation score for one or more computers that communicate the file. Curating the threat intelligence data in this regard can allow for more intuitive association of the data for threat identification, rating, or other analysis, whether by a data analyst analyzing the data, a machine-learning model being applied to the data, or other person or device capable of obtaining and processing the threat intelligence data (e.g., via a user interface or API).

In addition, by curating the threat intelligence data in this regard, additional improvements are provided at least by allowing analysis of the threat intelligence data. For example, the curated threat intelligence data can be used for subsequent analysis to identify and/or rate threats. For example the curated threat intelligence data can be queried to determine one or more threats. In another example, counterfactual analysis can be performed using the curated threat intelligence data by allowing modification of one or more parameters (e.g., reputation score) associated with one or more entities to determine a modification of another reputation score of one or more other entities. Using the curated threat intelligence data allows for more intuitive updating of the reputation scores in these examples. For example, in performing the counterfactual analysis, the one or more parameters of the one or more entities (e.g., received via an interface) can be updated temporarily, and a belief propagation process can be executed based on the updated parameters (e.g., which may be similar to re-executing the curating process taking one or more parameter values as true). In addition, a machine-learning model can be trained with the threat intelligence data, which may result in an improved model at least by virtue of the threat intelligence data being curated or having normalized parameter values (e.g., normalized reputation scores).

Turning now to <FIG>, examples are depicted with reference to one or more components and one or more methods that may perform the actions or operations described herein, where components and/or actions/operations in dashed line may be optional. Although the operations described below in <FIG> are presented in a particular order and/or as being performed by an example component, the ordering of the actions and the components performing the actions may be varied, in some examples, depending on the implementation. Moreover, in some examples, one or more of the actions, functions, and/or described components may be performed by a specially-programmed processor, a processor executing specially-programmed software or computer-readable media, or by any other combination of a hardware component and/or a software component capable of performing the described actions or functions.

<FIG> is a schematic diagram of an example of a device <NUM> (e.g., a computing device) that can curate threat intelligence data for improved processing, detection, or other consumption thereof. In an example, device <NUM> can include a processor <NUM> and/or memory <NUM> configured to execute or store instructions or other parameters related to providing an operating system <NUM>, which can execute a data curating component <NUM>, services <NUM>, or other components/services. For example, processor <NUM> and memory <NUM> may be separate components communicatively coupled by a bus (e.g., on a motherboard or other portion of a computing device, on an integrated circuit, such as a system on a chip (SoC), etc.), components integrated within one another (e.g., processor <NUM> can include the memory <NUM> as an on-board component <NUM>), and/or the like. Memory <NUM> may store instructions, parameters, data structures, etc., for use/execution by processor <NUM> to perform functions described herein. In addition, processor <NUM> and/or memory <NUM> may include one or more components that are distributed over multiple devices (not shown) and/or each of the multiple devices can execute one or more of the described components.

Operating system <NUM> may include a data curating component <NUM> for collecting, storing, and/or curating threat intelligence data from multiple data sources <NUM> in a threat intelligence database <NUM>. For example, data curating component <NUM> can operate as a service, application, etc. on operating system <NUM> to receive the threat intelligence data from the various data sources <NUM> (e.g., via an application programming interface (API) request, a publish/subscribe request, etc.), to curate the threat intelligence data, as described further herein, to allow for retrieval and/or counterfactual analysis of the data, etc. For example, the data sources <NUM> may correspond to other devices with which the device <NUM> can communicate to receive threat intelligence data and/or data otherwise imported from such sources in real-time (or near real-time), in batch processing, etc. by another service. In an example, the data sources <NUM> can be managed by vendors associated with threat identification and/or remediation, such as virus scanning and/or removal software. The data sources <NUM> may also include blog posts or other content from which information about potentially malicious entities can be mined or otherwise discerned.

The threat intelligence data obtained from the data sources <NUM>, for example, may include an identification of certain types of entities that may pose a threat, such as one or more computers (e.g., identified by network address, such as IP address, domain, account, and/or the like), one or more files (e.g., identified by a file hash, file properties, email or other tag information, metadata, and/or the like), etc., such that a threat identifying component can scan a device to determine presence of the one or more files, communications with the one or more computers, etc. The threat intelligence data obtained from the data sources <NUM> may also include one or more related parameters, such as a reputation score for the entities indicating whether the entities more or less likely to pose a threat. For example, the reputation score can be a numeric value, a letter grading, or substantially any enumeration used to indicate a relative threat level.

In any case, data curating component <NUM> can collect the threat intelligence data from the multiple data sources <NUM> and can store the threat intelligence data (or data generated based on the threat intelligence data) in the threat intelligence database <NUM>. Data curating component <NUM>, in an example, can also curate the data stored in the threat intelligence database <NUM> to normalize one or more parameters in the threat intelligence data. For example, data curating component <NUM> can include an entity associating component <NUM> for determining an association between entities in the threat intelligence data, a belief propagating component <NUM> for updating the threat intelligence data of one or more entities based on updating one or more parameters of one or more other entities in the threat intelligence data. Data curating component <NUM> may also include a parameter updating component <NUM> for allowing updating of the one or more threat intelligence data parameters of the one or more entities for, or as a result of, performing of a belief propagation process by the belief propagating component <NUM>.

For example, given the threat intelligence data from multiple sources, entity associating component <NUM> can determine associations between entities (e.g., between files and devices), which may be indicated in the threat intelligence data as received, and belief propagating component <NUM> can execute a belief propagation process regarding the determined associations to normalize one or more parameters (e.g., reputation score) of the entities (or at least of one or more of the types of entities) in the threat intelligence data. In addition, for example, belief propagating component <NUM> can execute a belief propagation and can determine whether to expire the entity from the threat intelligence database <NUM> based at least in part on a corresponding parameter value (e.g., based on detecting a difference in multiple reputation scores reported for the entity that achieves or exceeds a threshold difference). In this example, such an entity can be expired from the threat intelligence database <NUM> and/or reassessed to possibly assign a different reputation score.

In addition, data curating component <NUM> can communicate with other components or services to facilitate analysis and/or modification of the threat intelligence data in the threat intelligence database <NUM>. For example, operating system <NUM> may include a querying service <NUM> that facilitates querying the threat intelligence database <NUM> to receive updated parameters for one or more of the entities (e.g., updated based on curating the data, executing a belief propagation algorithm based on modifying one or more parameters, etc.). In another example, operating system <NUM> may include a counterfactual analysis service <NUM> that facilitates modifying one or more parameters associated with one or more entities in the threat intelligence database <NUM> (e.g., at least temporarily and/or as part of a copy of data in the database) such that belief propagating component <NUM> can execute the belief propagation process based on the modified parameter(s) to update one or more other parameters for one or more other entities, which can allow for evaluating changes to the threat identification data based on modifying the one or more parameters.

One or more applications <NUM> operating on the device <NUM> or other devices can execute and leverage the services <NUM>, <NUM> via one or more APIs, and/or the like. For example, the application <NUM> can execute an instance of the service <NUM>, <NUM> via a webpage in a web browser, an application that communicates with the service <NUM>, <NUM>, and/or the like, on a device (not shown) that may be remotely located from the device <NUM>. For example, the data curating component <NUM> may be operable for initializing and executing the service <NUM>, <NUM> or one or more contexts of the service, configuring one or more parameters for executing the service <NUM>, <NUM> etc. For example, a context of the service can relate to an execution of the service <NUM>, <NUM> given a set of parameters configured by the data curating component <NUM>.

Operating system <NUM> may also include a machine learning component <NUM> for training machine learning models based on the curated data in the threat intelligence database <NUM>. For example, the trained machine learning models may be used to identify possible threats based on detecting trends in associations among the entities indicated by the threat intelligence data.

<FIG> is a flowchart of an example of a method <NUM> for curating reputation scores of entities in threat intelligence data. For example, method <NUM> can be performed by a device <NUM>, and/or one or more components thereof, to update/normalize parameters of, or otherwise curate, the threat intelligence data.

In method <NUM>, at action <NUM>, threat intelligence data can be received, where the threat intelligence data includes a list of a first type of entities, a list of a second type of entities, one or more associations between the entities, and a reputation score for each of the entities. In an example, data curating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, etc., can receive the threat intelligence data (e.g., from multiple data sources <NUM>) including the list of a first type of entities, the list of a second type of entities, the one or more associations between the entities, and the reputation score for each of the entities. The threat intelligence data can include parameters for other types of entities as well, may include additional parameters other than reputation score, etc. In addition, the one or more parameters (e.g., including reputation score) may be proprietary to the data source from which the data is received or otherwise not normalized among data sources <NUM>. Data curating component <NUM> can store the threat intelligence data in the threat intelligence database <NUM> for subsequent curating, analysis, or other manipulation, etc., as described herein.

In a specific example, the list of the first type of entities may include a list of devices identified by one or more device parameters, such as a network address (e.g., IP address, media access control (MAC) address, etc.), and the list of the second type of entities may include a list of files identified by one or more file parameters, such as a file hash value computed by applying a hash function to at least a portion of the file. Each of the devices and files in the received threat intelligence data may have an associated reputation score that indicates a relative threat determined for the entity (e.g., as a Boolean value representing a possible threat or not a possible threat, a percentage or other floating point value between <NUM> and <NUM> representing a predicted degree of threat, a letter grade or numeral value representing the predicted degree of threat, etc.). For example, the reputation score may indicate a likelihood that the entity is malicious or is associated with potentially malicious activity.

In addition, the threat intelligence data received from the multiple data sources <NUM> may include an indication of association between ones of the different types of entities (e.g., between ones of the first type of entities and the second type of entities). For example, the association(s) may include one-to-one associations between different types of entities, one-to-many associations between an entity of a first type and multiple entities of a second type (and/or other types). In the specific example above, the threat intelligence data may identify an association between a device and a file (and/or between the device and multiple files) for multiple ones of the devices. In addition, the threat intelligence data may identify an association between a file and a device (and/or between the file and multiple devices) for multiple ones of the files. In one specific example, the threat intelligence data can be received as, or otherwise interpreted by the entity associating component <NUM> as, a bipartite graph including nodes for each of the entities (e.g., a first set of nodes for the devices and a second set of nodes for the files) with edges connecting ones of the first set of nodes to ones of the second set of nodes to indicate the associations. Thus, for example, data curating component <NUM> can store the threat intelligence data in a format consistent with a bipartite graph (e.g., a list of the nodes and a list of edge information for connecting the nodes).

In method <NUM>, optionally at action <NUM>, a confidence value corresponding to the one or more associations can be received. In an example, entity associating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can receive the confidence value corresponding to the one or more associations. In an example, the entity associating component <NUM> can generate the confidence value for the one or more associations based on various considerations regarding the threat intelligence data, which may include a data source (or sources) <NUM> from which the threat intelligence data is received (e.g., sources deemed more reliable can result in higher associated confidence values for the indicated associations, similar association data from multiple sources can result in a higher confidence value, etc.). In an example, entity associating component <NUM> can store the confidence value in the threat intelligence database <NUM> with the threat intelligence data (e.g., as associated with the association between entities).

In method <NUM>, at action <NUM>, an updated reputation score can be determined for at least one of the first type of entities based at least in part on the confidence value and/or on determining a reputation score for at least one of the second type of entities. In an example, belief propagating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can determine the updated reputation score for at least one of the first type of entities (e.g., a device or file) based at least in part on the confidence value and/or on determining the reputation score for at least one of the second type of entities (e.g., a file where the first type is device, or a device where the first type is file).

In an example, belief propagating component <NUM> can update the reputation score for one or more of the entities as part of a belief propagating process where the belief propagating component <NUM>, based on a set of parameters taken as true for a set of entities, can update the parameters for other entities. For example, in performing the belief propagating process, the belief propagating component <NUM> can determine, for a first entity having a parameter (e.g., reputation score) taken as true, a second entity associated with the first entity (e.g., based on a stored association). Based on the reputation score of the first entity and the confidence value for the association (and/or the original reputation score for the second entity), the belief propagating component <NUM> can update the reputation score for the second entity. In another example, belief propagating component <NUM> can additionally or alternatively update the reputation score for the second entity based on a number of entities of the first type that are associated with the second entity, the corresponding reputation scores for the entities of the first type, etc. In any case, for example, when the second entity reputation score is updated, it can be used to update the reputation score for other entities, and so on, until the belief propagation process is completed. Moreover, the reputation score of the first entity can be used to update reputation scores for other entities (e.g., entities of the second type, entities of the first type that may be associated with similar entities of the second type, etc.). In addition, for example, various entities can be considered as having true reputation score values for the purpose of propagating the belief(s) throughout the threat intelligence data.

In method <NUM>, at action <NUM>, the reputation score of the at least one of the first type of entities can be updated to the updated reputation score. In an example, parameter updating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can update the reputation score of the at least one of the first type of entities to the updated reputation score. For example, parameter updating component <NUM> can update the reputation score associated with the entity in the threat intelligence database <NUM>. In another example, parameter updating component <NUM> can update the reputation score associated with the entity in a copy of the threat intelligence database <NUM>, or other temporary storage, to facilitate counterfactual analysis, as described herein. In any case, curating the threat intelligence data in this regard can facilitate querying of the data having normalized parameter values to allow for more accurate data analysis/manipulation. In some examples, detecting a threshold difference or inconsistency in reported and/or normalized parameter values can cause parameter updating component <NUM> to expire an entity from the threat intelligence database <NUM>. In one example, the entity may be reevaluated in subsequent data curation where it is again received from one or more data sources <NUM>.

In method <NUM>, optionally at action <NUM>, a query for the at least one of the first type of entities can be received. In an example, data curating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, etc., can receive the query for the at least one of the first type of entities (e.g., via querying service <NUM>). For example, the querying service <NUM> can provide an interface to facilitate querying the threat intelligence data (e.g., in the form of a user interface provided on an application <NUM>, an API for programmatically requesting and receiving threat intelligence data via the application <NUM>, etc.).

In response to the query, optionally at action <NUM>, the updated reputation score for the at least one of the first type of entities can be retrieved from the threat intelligence data. In an example, data curating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, etc., can retrieve the updated reputation score for the at least one of the first type of entities (e.g., along with other data that may correspond to the query). For example, data curating component <NUM> can retrieve the reputation scores and/or associated data by querying the threat intelligence database <NUM>.

In addition, in an example, optionally at action <NUM>, the updated reputation score can be displayed in response to the query. In an example, application <NUM>, e.g., in conjunction with a processor, memory, operating system, etc. of a device executing the application <NUM>, can display the updated reputation score in response to the query. Thus, in this example, a data analyst can retrieve, view, and consume the updated reputation score in performing analysis of the threat intelligence data from the various sources.

In another example, optionally at action <NUM>, one or more machine learning models can be trained based on the updated reputation score. In an example, machine learning component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, etc., can train the one or more machine learning models based on the updated reputation score. For example, machine learning component <NUM> can use the updated reputation score along with other input parameters (e.g., the confidence values for associations between entities, etc.) to the machine learning model to train the model. Using the machine learning model, for example, the machine learning component <NUM> may identify potential additional threats based on the threat intelligence data, and/or may indicate the identified potential threats via an interface.

For example, based on the trained model, a homophilic property may be observed where a device determined to be a potential threat may also have multiple files determined to be potential threats. Thus, the machine learning component <NUM> may use this property to identify other devices with the same or similar files, a similar number of multiple potentially threatening files, etc. to determine the device as posing a potential threat. In one example, the aforementioned belief propagation process may be performed based on the trained model. In this example, based on the trained model, the belief propagating component <NUM> may identify a file with an unsure reputation score (e.g., <NUM> on a scale of <NUM> to <NUM>) associated with a device having a potentially threatening reputation score (e.g., closer to <NUM>). In this example, belief propagating component <NUM> can lower the reputation score of the file in the threat intelligence database <NUM>.

In another example, the machine learning component <NUM> may observe, based on the trained model, an accuracy of scoring an entity as a threat by a certain data source <NUM> as being higher than other sources, and machine learning component <NUM> may accordingly increase confidence values in associations related to the certain data source <NUM>, increase (or decrease) reputation scores for entities reported by the certain data source <NUM>, etc. to use machine learning in analyzing the threat intelligence data.

<FIG> is a flowchart of an example of a method <NUM> for facilitating counterfactual analysis of curated reputation scores of entities in threat intelligence data. For example, method <NUM> can be performed by a device <NUM>, and/or one or more components thereof, to initiate a counterfactual scenario for determining an outcome associated with the threat intelligence data.

Method <NUM> can include actions <NUM> and <NUM>, as described above in reference to method <NUM> of <FIG>. Thus, at action <NUM>, threat intelligence data can be received, where the threat intelligence data includes a list of a first type of entities, a list of a second type of entities, one or more associations between the entities, and a reputation score for each of the entities. In an example, data curating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, etc., can receive the threat intelligence data (e.g., from multiple data sources <NUM>) including the list of a first type of entities, the list of a second type of entities, the one or more associations between the entities, and the reputation score for each of the entities, as described. In addition, optionally at action <NUM>, a confidence value corresponding to the one or more associations can be received. In an example, entity associating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can receive the confidence value corresponding to the one or more associations.

In method <NUM>, at action <NUM>, an updated reputation score can be received for at least one of the first type of entities. In an example, parameter updating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can receive the updated reputation score for the at least one of the first type of entities. As described, for example, data curating component <NUM> can communicate with a counterfactual analysis service <NUM> to facilitate modification of reputation scores or other parameters associated with one or more entities (e.g., by providing an interface to or via an application <NUM>). In this regard, the counterfactual analysis service <NUM> may allow a user to set the parameter value for the purposes of performing counterfactual analysis and/or to permanently (or semi-permanently) set the parameter value in the threat intelligence database <NUM>. In another example, counterfactual analysis service <NUM> can make a copy of the database or otherwise separately track modifications so the threat intelligence database <NUM> is not actually modified for the purpose of the counterfactual analysis. In any case, parameter updating component <NUM> can update the parameter based on input from the counterfactual analysis service <NUM>. In addition, parameter updating component <NUM> may update multiple parameter values for multiple entities in this regard.

In method <NUM>, at action <NUM>, a second updated reputation score for at least another one of the first type of entities or at least one of the second type of entities can be determined based at least in part on the updated reputation score and/or on the confidence value. In an example, belief propagating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can determine the second updated reputation score for at least another one of the first type of entities or the at least one of the second type of entities based at least in part on the updated reputation score (for the at least one of the first type of entities) and/or on the confidence values. For example, belief propagating component <NUM> can determine the second updated reputation score as part of a belief propagation process, as described above, based at least in part on taking the updated reputation score for the at least one of the first type of entities as true. In one example, the belief propagation process may be initiated based on updating of the reputation score, a command from the counterfactual analysis service <NUM> to start the process (e.g., which may be based on a command received via a user interface provided to or by an application <NUM>, etc.).

In method <NUM>, at action <NUM>, the reputation score of the at least another one of the first type of entities or the at least one of the second type of entities can be updated to the second updated reputation score. In an example, parameter updating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can update the reputation score of the at least another of the first type of entities or the at least one of the second type of entities to the second updated reputation score. For example, parameter updating component <NUM> can update the reputation score associated with the entity in the threat intelligence database <NUM>, or in a copy thereof, or in another mechanism for tracking changes to the threat intelligence database <NUM>, such that the threat intelligence database <NUM> data is not necessarily impacted by the update or can otherwise be restored to a state prior to executing the belief propagation process. In any case, curating the threat intelligence data based on the indicated belief for one or more parameters can facilitate counterfactual analysis of the data to determine changes that may result if beliefs of threat level or other parameters (e.g., reputation score) for certain entities are taken as true. In other examples, other graph processes may be similarly performed, such as graph centrality scoring, shortest path processes, label propagation processes, etc. In one example, as described further herein, where receiving the updated reputation score includes an analyst updating the reputation score and/or adding entities to the graph in performing counterfactual analysis, graph centrality scoring changes can be evaluated to determine how the additional entity and/or updated reputation score impacts other nodes in the graph. Additionally, a clustering technology typically associated with such graph processes can be used to execute such processes to provide high performance of processing and modifying values of the graph.

In method <NUM>, optionally at action <NUM>, the second updated reputation score can be displayed as associated with the at least another one of the first type of entities or the at least one of the second type of entities. In an example, application <NUM>, e.g., in conjunction with a processor, memory, operating system, etc. of a device executing the application <NUM>, can display the second updated reputation score in response to a query or in response to modification of a parameter value of an entity in the threat intelligence data, as described above. Thus, in this example, a data analyst can retrieve, view, and consume the updated reputation score in performing counterfactual analysis of the threat intelligence data.

In method <NUM>, optionally at action <NUM>, the threat intelligence data can be updated based on the updated reputation score and the second updated reputation score. In an example, parameter updating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can update the threat intelligence data based on the updated reputation score and the second updated reputation score. For example, parameter updating component <NUM> can update the threat intelligence database <NUM> with the updated reputation score and/or the second updated reputation scores(s) revised based on performing the belief propagation process based on the updated reputation score. Thus, for example, where an analyst determines the updated reputation score is accurate with high confidence (e.g., based on the counterfactual analysis), the results can be modified in the threat intelligence database <NUM> in this regard (e.g., and used in subsequent analysis).

In a specific example, where the first type of entity is a device, the parameter updating component <NUM> can receive an updated reputation score for at least one of the devices (e.g., via a user interface provided by a counterfactual analysis service <NUM>, etc.), and parameter updating component <NUM> can update the reputation score for the entity in the threat intelligence database <NUM> (or a copy thereof or separate changes tracking mechanism, as described). In this example, belief propagating component <NUM> can then execute a belief propagating process to determine updated reputation values for other entities based on updating the reputation score for the at least one device. For example, belief propagating component <NUM> can determine whether to accordingly update reputation scores for one or more files having an indicated association with the at least one device, whether to accordingly update reputation scores for one or more devices having an indicated association with the one or more files whose reputation scores may have been updated, and so on until the process completes. In addition, parameter updating component <NUM> can allow for updating parameter values associated with files as well, and belief propagating component <NUM> can similarly determine updated reputation scores or other parameter values for other devices associated with the files and/or files associated with the associated devices, etc..

In a specific example, data curating component <NUM> can provide a representational state transfer (REST) API that allows the querying service <NUM> to request retrieval of data related to one or more specific entities. In another example, the REST API can allow counterfactual analysis service <NUM> to request modification of the parameter values in the threat intelligence database <NUM>, run the belief propagation process, obtain revised parameter values for other entities based on modifying the parameter value, etc. As described, such modification can be made to a copy of the threat intelligence database <NUM> or other mechanism for making temporary changes so the user need not worry about overwriting data in the threat intelligence database <NUM> in performing the counterfactual analysis.

Additionally, in a specific example, the threat intelligence database <NUM> can be a graph database for storing a representation of a bipartite graph, as described above. Using a graph database can facilitate efficient query execution on a large graph while using minimal compute resources. For example, several collections within the graph database can be used - e.g., one collection for maintaining the entire threat intelligence graph (as a primary data source), other collections for reading/writing results of counterfactual analysis, etc. This separation allows an analyst to repeatedly modify graph entities during counterfactual analysis without worrying about changing the original data source. In one example, upon making a high-confidence assessment, the analyst can choose to store results into the original threat intelligence database <NUM> (e.g., by updating the threat intelligence database <NUM> as described in reference to action <NUM>). In this regard, the counterfactual analysis service <NUM> may also provide a function to modify the data in the actual threat intelligence database <NUM> (e.g., which can be provided as an option via application <NUM>).

<FIG> is a flowchart of an example of a method <NUM> for facilitating updating confidence values of associations between entities based on feedback. For example, method <NUM> can be performed by a device <NUM>, and/or one or more components thereof, to update a confidence value corresponding to an association between entities.

Method <NUM> can include actions <NUM>, <NUM>, <NUM>, <NUM>, as described above in reference to method <NUM> of <FIG>. Thus, at action <NUM>, threat intelligence data can be received, where the threat intelligence data includes a list of a first type of entities, a list of a second type of entities, one or more associations between the entities, and a reputation score for each of the entities. In an example, data curating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, etc., can receive the threat intelligence data (e.g., from multiple data sources <NUM>) including the list of a first type of entities, the list of a second type of entities, the one or more associations between the entities, and the reputation score for each of the entities, as described. In addition, at action <NUM>, a confidence value corresponding to the one or more associations can be received. In an example, entity associating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can receive the confidence value corresponding to the one or more associations. Also, at action <NUM>, an updated reputation score can be determined for at least one of the first type of entities based at least in part on the confidence value and/or on determining a reputation score for at least one of the second type of entities. In an example, belief propagating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can determine the updated reputation score for at least one of the first type of entities (e.g., a device or file) based at least in part on the confidence value and/or on determining the reputation score for at least one of the second type of entities. Additionally, at action <NUM>, the reputation score of the at least one of the first type of entities can be updated to the updated reputation score. In an example, parameter updating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can update the reputation score of the at least one of the first type of entities to the updated reputation score.

In method <NUM>, at action <NUM>, updated threat intelligence data can be received. In an example, data curating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, etc., can receive the updated threat intelligence data, which can include receiving threat intelligence data from one or more data sources <NUM> for storing in the threat intelligence database <NUM>, as described herein. For example, the updated threat intelligence data can be similarly formatted to the threat intelligence data received at action <NUM>, but may apply to a subsequent period of time, and may thus include newer data on potential threats.

In method <NUM>, at action <NUM>, the confidence value can be updated based at least in part on determining a difference between the updated reputation score and a new reputation score for the at least one of the first type of entities in the updated threat intelligence data. In an example, parameter updating component <NUM>, e.g., in conjunction with processor <NUM>, memory <NUM>, operating system <NUM>, data curating component <NUM>, etc., can update the confidence value based at least in part on determining the difference between the updated reputation score and the new reputation score for the at least one of the first type of entities in the updated threat intelligence data. For example, where the updated reputation score, updated as part of the belief propagation process, is similar to the newly received reputation score, this may indicate the confidence in the association between entities (e.g., that resulted in the updated reputation score) is correct or nearly correct (e.g., and/or can be a function of the difference). In this regard, parameter updating component <NUM> can accordingly update the confidence value based on the feedback of the updated threat intelligence data to reflect a confidence of the association between entities. For example, the updating of the confidence value can occur based on receiving the updated threat intelligence data. For example, parameter updating component <NUM> may update the confidence value as stored in or with the threat intelligence database <NUM>.

<FIG> illustrates an example of device <NUM>, similar to or the same as device <NUM> (<FIG>) including additional optional component details as those shown in <FIG>. In one implementation, device <NUM> may include processor <NUM>, which may be similar to processor <NUM> for carrying out processing functions associated with one or more of components and functions described herein. Processor <NUM> can include a single or multiple set of processors or multi-core processors. Moreover, processor <NUM> can be implemented as an integrated processing system and/or a distributed processing system.

Device <NUM> may further include memory <NUM>, which may be similar to memory <NUM> such as for storing local versions of applications being executed by processor <NUM>, such as a data curating component <NUM>, an operating system <NUM>, other components thereof, applications (e.g., applications <NUM>), service(s) <NUM> (e.g., querying service <NUM>, counterfactual analysis service <NUM>, etc.), related instructions, parameters, etc. Memory <NUM> can include a type of memory usable by a computer, such as random access memory (RAM), read only memory (ROM), tapes, magnetic discs, optical discs, volatile memory, nonvolatile memory, and any combination thereof.

Further, device <NUM> may include a communications component <NUM> that provides for establishing and maintaining communications with one or more other devices, parties, entities, etc., utilizing hardware, software, and services as described herein. Communications component <NUM> may carry communications between components on device <NUM>, as well as between device <NUM> and external devices, such as devices located across a communications network and/or devices serially or locally connected to device <NUM>. For example, communications component <NUM> may include one or more buses, and may further include transmit chain components and receive chain components associated with a wireless or wired transmitter and receiver, respectively, operable for interfacing with external devices.

Additionally, device <NUM> may include a data store <NUM>, which can be any suitable combination of hardware and/or software, that provides for mass storage of information, databases, and programs employed in connection with implementations described herein. For example, data store <NUM> may be or may include a data repository for applications and/or related parameters (e.g., data curating component <NUM>, an operating system <NUM>, other components thereof, applications, etc.) not currently being executed by processor <NUM>. In addition, data store <NUM> may be or include a data repository for data curating component <NUM>, such as the threat intelligence database <NUM>, other components thereof, applications, and/or one or more other components of the device <NUM>.

Device <NUM> may include a user interface component <NUM> operable to receive inputs from a user of device <NUM> and further operable to generate outputs for presentation to the user. User interface component <NUM> may include one or more input devices, including but not limited to a keyboard, a number pad, a mouse, a touch-sensitive display, a navigation key, a function key, a microphone, a voice recognition component, a gesture recognition component, a depth sensor, a gaze tracking sensor, a switch/button, any other mechanism capable of receiving an input from a user, or any combination thereof. Further, user interface component <NUM> may include one or more output devices, including but not limited to a display, a speaker, a haptic feedback mechanism, a printer, any other mechanism capable of presenting an output to a user, or any combination thereof.

Device <NUM> may additionally include and/or be communicatively coupled with a data curating component <NUM>, one or more services <NUM> (which may include a querying service <NUM>, counterfactual analysis service <NUM>, machine learning component <NUM>, etc.), components thereof, etc. for providing the functionalities described herein.

Accordingly, in one or more implementations, one or more of the functions described may be implemented in hardware, software, firmware, or any combination thereof. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), and floppy disk where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.

Claim 1:
A computer implemented method for curating threat intelligence data, comprising:
receiving from multiple data sources (<NUM>) threat intelligence data comprising a list of a first type of entities, a list of a second type of entities, an indication of an association between at least one of the first type of entities and at least one of the second type of entities, and a reputation score for each entity in the list of the first type of entities and the list of the second type of entities, the reputation score indicating a relative threat level of that entity in posing a security threat, wherein the first type of entity is an internet protocol, IP, address and wherein the second type of entity is a file hash;
receiving a confidence value corresponding to the association wherein the confidence value indicates reliability of the association;
determining an updated reputation score for the at least one of the first type of entity based on the confidence value and the reputation score of the at least one of the second type of entity to which the at least one of the first type of entities is indicated to be associated in the one or more associations;
updating, in the threat intelligence data (<NUM>), the reputation score of the at least one of the first type of entities to the updated reputation score; and
training, based on the threat intelligence data comprising the updated reputation score, a machine learning model to identify possible threats based on detecting trends in associations among the entities indicated by the threat intelligence data.