Patent ID: 12206682

DETAILED DESCRIPTION

The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

The embodiments herein disclose a security appliance to monitor one or more software defined infrastructures. Referring now to the drawings, where similar reference characters denote corresponding features consistently throughout the figures, various examples of this disclosure are described.

FIG.1depicts an example network computing environment100. The network computing environment100may have a plurality of cloud computing resources, for example, cloud computing resources102A,102B and102C, all connected to internet104, over link106. Cloud computing resources102A,102B and102C may be similar, in the sense provided by a single vendor, for example, Amazon or Microsoft. In some examples, the cloud computing resources102A,102B and102C may be dissimilar, for example, the cloud computing resource102A may be provided by Amazon, cloud computing resource102B may be provided by Microsoft and cloud computing resource102C may be provided by yet another vendor.

Each of the cloud computing environments102A,102B and102C may include one or more software defined infrastructures (SDIs). For example, the cloud computing environment102A has SDIs108A-108D deployed or running, the cloud computing environment102B has SDIs108E-108H deployed or running, and the cloud computing environment102C has SDI108J deployed or running. As one skilled in the art appreciates, one or more of the SDIs may be deployed automatically, with minimal or no interaction from an administrator, based on one or more rules. For example, these rules may define the amount and type of computing resources to be allocated to a particular SDI. In some examples, the amount and type of computing resources allocated to a particular SDI may dynamically change, due to changing demands on the computing resource.

The security appliance110of this disclosure may be executed on any of the SDIs. In one example, the security appliance110is executed on the SDI108J, provisioned on cloud computing environment102C. The security appliance may be configured to monitor one or more SDIs provisioned on cloud computing environments. For example, the security appliance110may be configured to monitor one or more of SDI108A-108D running on cloud computing environment102A, one or more of SDI108E-108H running on cloud computing environment102B.

FIG.2shows an example block diagram of a security appliance110. The security appliance110includes a data ingestion and query engine112(DIQ engine), a machine learning engine114(ML engine), a policy compliance engine116(PC engine), a visualization engine118, a notification engine120, a remediation engine122and a user interface124.

The security appliance110is configured to communicate with a SDI, for example, SDI108A, over link106. In one example, the security appliance110communicates with SDI108A using a SDI API126. The security appliance110is also configured to communicate with a user computer128, over link130. In some examples, the link130may be a link over internet104, for example, link106shown inFIG.1. In some examples, the user interface124of the security appliance110may communicate with a rendering engine132executed on the user computer128.

The DIQ engine112is configured to receive raw metadata about assets, audit events and network flow of a SDI that is being monitored by the security appliance110. For example, the DIQ engine112may access various data sources of the SDI108A, using the SDI API126. The DIQ engine112also provides a query interface to access various data stored or accessible by the DIQ engine112. Functions and features of the DIQ engine112will be further described in detail, with reference toFIG.3.

The policy compliance engine114interacts with the DIQ engine112to receive various records and attributes related to assets of the SDI108A. The policy compliance engine114further compares policies set for various assets of the SDI108A against actual attributes of various assets of the SDI108A. As one skilled in the art appreciates, in some examples, a scan of the assets for compliance may be triggered based on a preset schedule set by a user. In some examples, the scan of the assets for compliance may be triggered in response to a predefined activity related to an asset, for example, when a new asset is deployed. During a scan for compliance, configurations of each of the assets since the time of the last scan are compared to the defined policies for that type of asset. Based on the comparison, the policy compliance engine116determines any deviation from the set policies and generates appropriate violation reports.

In some examples, the violation reports are consumed by the notification engine120to generate appropriate messages to a user or administrator of the SDI108A to communicate to the user details of the violations for further action. The notification engine120may dispatch details of violations by various methods. For example, in some examples, details of violations may be communicated to a user using e-mail, pager, text message and the like. In some examples, a message indicative of the violation may be configured to be submitted to an external ticketing system, for further action. In some examples, the message indicative of the violation may be submitted to an aggregation system configured to receive various violation reports from a plurality of SDIs monitored by the security appliance110.

In some examples, the violation report may trigger an automatic corrective action for an asset of the SDI108A, triggered by the remediation engine122. The remediation engine122is configured to interact with the SDI108A over the SDI API126. The corrective action may include adjusting the configuration of an asset of the SDI108A so that it matches a desired state, or injecting firewall rules to isolate an asset of the SDI108A. This feature will be further explained in detail with reference toFIG.11.

The machine learning engine114is configured to evaluate various change events for an asset and infer certain relationships between these events, for example, using sequential pattern mining. This feature will be further explained in detail later with reference toFIG.8. In some examples, the machine learning engine114is configured to flag certain audit events as higher priority based on deviation from a baseline behavior for actions committed by a user. This feature will be further explained in detail later with reference toFIG.9. In some examples, the machine learning engine114may automate grouping of assets by similarity of workload based on network flow data. This feature will be further explained in detail later with reference toFIG.10. In some examples, the machine learning engine114may detect and flag an anomalous network activity. This feature will be further explained in detail later with reference toFIG.10.

Visualization engine118is configured to convert results provided by the DIQ engine112into a visualization record. The visualization record may contain data to render a directed graph, for example, a graph with nodes and edges. Nodes represent elements of a result, for example, search results that are grouped together using rules. Rules may be provided by a user or may be set as a default parameter in the security appliance110. An example rule may be, “group elements having the same value for a given attribute (or tag) property with name=X. Edges between nodes represent some activity between one node and another node.

For example, in a network visualization, edges may represent flow of data between two nodes representing a computing device. In some examples, the edges may be directional. For example, in a network visualization, direction of the edge may indicate which node initiated the communication. In the case of an audit visualization, edges may represent actions, with the direction of edge indicative of which node is the subject and which node is the object. The graph may further include metadata for each node and edge. For example, in a network visualization, node metadata may indicate names and IP address of hosts within a node. Edge metadata may indicate traffic volumes, destination port number, and traffic classification and the like. An example of a directed graph will later be described in detail.

In some examples, a rendering engine132may be configured to receive the visualization record and present the visualization record in a human readable form on a display device, for example, a display device of the user computer128. In some examples, the rendering engine132may be a program executed on a browser of the user computer128.

Now, referring toFIG.3, an example DIQ engine112is further described. The DIQ engine112includes a data ingestion agent302, a data ingestion engine304, a network flow analysis engine306, a low latency data store308, a bulk data store310, an aggregated network flow data store312and a query interface314.

The data ingestion agent302is configured to communicate with external data stores and retrieve data into the DIQ engine112. For example, the data ingestion agent302may communicate with one or more data stores of a SDI, for example, SDI108A. For example, an asset configuration data store316, an audit event data store318and a network log data store320.

The asset configuration data store316may have details about various assets deployed in the SDI. For example, virtual machine (VM) running containers, images used to launch containers, images used to launch VM instances, virtual network interfaces, virtual network subnets, managed data bases, managed flow bouncers. Additionally, the asset configuration data store316may also contain details about users, infrastructure, reports of user activities and the like.

The audit event data store318may contain notifications generated by the SDI when a user takes some infrastructure activity. For example, an infrastructure activity like launching, stopping or deleting infrastructure. Infrastructure activity may also include logging into a control console of the SDI and making modifications to the infrastructure. The network log data store320may contain records of network traffic within the SDI as well as network traffic over the internet.

In some examples, the data ingestion agent302may communicate with SDI API126of the SDI108A to access and retrieve data from asset configuration data store316, audit event data store318and network log data store320. In some examples, the SDI108A may not provide an API to access various SDI data stores. In such examples, an agent (not shown) may be run in the SDI108A to access various SDI data stores.

In some examples, the data ingestion agent302may also communicate with external data sources like a network threat intelligence feed322and geo location data store324. The network threat intelligence feed322may provide IP addresses associated with suspicious or malicious activities. The geo location data store324may provide mapping of IP addresses to physical location. For example, country, state, county and city associated with the IP address may be provided. In some examples, IP addresses associated with an IP service provider are also provided.

The data ingestion agent302retrieves or collects data from various data stores. The format of the data received may be proprietary to a specific SDI or source. The data ingestion agent302normalizes the received data into a known format and provides the normalized data to the data ingestion engine304.

The data ingestion engine304processes the received normalized data from the data ingestion agent302. Data related to network flow may be further processed by the network flow analysis engine306. The network flow analysis engine306determines which party initiated the network communication. Network flow analysis engine306may also use one or more rules to deduce the roles of a party to a network communication. For example, if a network communication occurred from a server over port80, then, the network communication is an indication of communication over the internet. In other words, a network communication that occurred to an external system. This information may be appropriately stored with the network communication data. For example, aggregated network flow data may be stored in the aggregated network flow data store312. In some examples, the aggregated network flow data store312may be a large volume data store that may provide reasonable access to retrieve stored data in response to a query. Raw network flow data is stored in a bulk data store310. Data stored in the bulk data store310may be used by background processes executed on the security appliance110, For example, the machine learning engine114of the security appliance110may use data stored in the bulk data store310to analyze data.

Information related to asset configuration and audit event data are stored in a low latency data store308. Low latency data store310is configured to provide quick access to stored data, with minimal latency. Some of the asset configuration and audit event data may also be stored in the bulk data store310. Data from network threat intelligence feed322and geo location data store324may be serialized by the data ingestion engine304and stored in the low latency data store308.

The query interface314of the DIQ engine112is configured to receive requests from external processes or engines. For example, requests may be received from policy compliance engine116, machine learning engine114or the user interface124of the security appliance110. The query interface314is configured to provide a set of query APIs with specialized functions as well as interpreters for certain domain specific query language. The query interface312examines the query input and determines what type of data is required to satisfy the query. Based on the determination, the query interface312composes appropriate queries to specific data store, for example, one or more of the low latency data store308, bulk data store310and aggregated network flow data store312.

As an example, for a query request requesting network data related to assets that are tagged with a “production environment” tag, the query interface312may generate a sub-query to low latency data store308to retrieve IP addresses for all assets that carry a tag of “production environment”. The retrieved results of that sub-query from low latency data store308(which are IP addresses) may be used by the query interface312to generate another query to the aggregated network flow data store312to retrieve only those network flow data corresponding to the retrieved IP addresses. The retrieved network flow data is then returned as the result for the initial query by the query interface312. As one skilled in the art appreciates, the retrieved network flow data corresponds to network data related to assets that are tagged with a “production environment”.

As another example, the query interface312may receive a query to provide a network graph of all assets in a particular virtual network. In this example, the query interface312will generate a query to the aggregate network flow data store312to retrieve all network traffic related to the requested particular virtual network. The assets of the retrieved network traffic are then mapped to a corresponding IP address based on the data stored in the low latency data store308, for example, by generating a series of appropriate queries by the query interface312to the low latency data store308. Then, the network traffic information and asset configuration details are sent to the visualization engine118of the security appliance110. The visualization engine118will communicate with the rendering engine132to generate and present the network graph on a display device of the user computer128as previously described with reference toFIG.2.

In some environments, business units or users may be permitted to deploy assets within a virtual environment, for example, a SDI automatically, based on their needs. Once the needs are met, the deployed assets are decommissioned promptly. These decommissioned assets may be sometimes referred to as ephemeral assets, as they are commissioned and decommissioned within a short period of time. Generally, details of the deployed and later decommissioned assets (ephemeral assets) are available for a short period after the decommissioning, for example, an hour after the decommissioning. Thereafter, the SDI may not retain information related to these ephemeral assets. As the security appliance110of this disclosure periodically accesses various data stores of the SDI, for example, the asset configuration data store315, audit event data store318and network log data store320, retrieves the data and stores corresponding data in the data stores of the DIQ engine112, a user can query the data stores of the DIQ engine112at a later time to determine various activities that occurred in a SDI over a period of time, even as related to ephemeral assets.

In some examples, one or more queries may be stored in a database for later use. In some examples, it may be desirable to generate or craft a query in real time, based on various attributes. One or more of these queries may refer to a policy to be implemented for the software defined infrastructure previously described with reference toFIGS.1and2. In some examples, a query may be referred to as a rule. An example dynamic policy generator330(sometimes referred to as DP generator) is described with reference toFIG.3A. As one skilled in the art appreciates the DP generator330may be part of the DIQ engine112. In some examples, the DP generator330may be part of the query interface314of the DIQ engine112.

Now, referring toFIG.3A, the DP generator330is described. The DP generator330includes a model builder332, an information retrieval engine334, a rule authoring system336, a rule compiler338. The DP generator330additionally has a rule evaluation engine340, a rules data store342and a mindata datastore344. The model builder332builds a model for each of the resources of the SDI for a given period of time. Data about each of the SDI may be represented in a tree structured data that may span many levels. The node in the tree may have a branching factor of one or more. The leaves of the tree contain one of the following value types: integer, string, float, boolean, double and null.

Data about each of the SDI is sampled over a predefined time period to enable building a navigation and interpretation model (sometimes referred to as “NI model” or just “model”). The model so built is a map between normalized paths in the tree structured resources and the type of value that may be found in the leaf. When dealing with array nodes in the tree, the model creates a normalized path that applies to all the children. Initially a pilot model is built, by sampling one of the records. The pilot model will include some fields that are fully specified and some fields with null values, marked as unknowns. The model builder scans additional samples to incrementally populate the model by performing union with fully specified fields and updating unknowns, either adding additional fields or updating fields with known value types. The model builder completes scanning all available samples to update the pilot model. The pilot model is considered to be a complete model when there are no null value fields in the model. This will be further explained with reference toFIGS.3B,3C and3D.

The information retrieval engine334is configured to retrieve data both from the model builder332and one or more databases316,318and320of the SDI108A. In some examples, the information retrieval engine334may retrieve data from one or more databases316,318and320of the SDI108A using data ingestion agent302of the data ingestion and query engine112, as previously described with reference toFIG.3. The information retrieval engine334generates one or more path documents in the retrieval system, for each of the resources. In one example, each path document may refer to data contained in a row of the model created by the model builder, with additional information fields. In one example, an address to a path document may be indexed for easy access. Each path document in the retrieval system contains a unique path and additional associated information like cloud type, resource name, service name, set of applicable operators and suggested values. The cloud type, resource name and service name are derived from the mappings provided in the database schemas of the SDI. The applicable operators are extracted from the normalized paths and the type of value stored against that path.

For a single value field, normalization is not required. For array paths, normalization refers to converting the path to represent a generic node that captures all of the children under that node. Field value type could be numeric, boolean, array or object specific. Relevant operators apply both for the completed path (up to the leaf) of the tree as well as partial paths (intermediate nodes).

The rule authoring system336is configured to provide context sensitive help to a user, when a rule is being formed by a user. For example, the rule authoring system336monitors keywords entered by the user in a query editor presented to the user on an input-output device. The rule authoring system336evaluates the keywords entered by the user and provides help or guidance to the user. The guidance provided to a user may include extending field names, selecting operators, selecting values, extending to additional conditions and performing joins across resources. As the rule formation continues, the rule authoring system336continuously validates the rule for acceptance.

The rule compiler338communicates with the rule authoring system336to present possible rule completion scenarios to a user. As the rule construction progresses, the existing string from the query editor is sent to the rule complier338. If a rule is incomplete, the rule compiler338returns possible completion scenarios back to the rule authoring system336, which is presented to the user. The cycle of editing and compilation of the rule continues until the rule is completed as per defined grammar.

The rule compiler338in one example, contains finite automatons generated from two levels of grammar rules. First level of automaton is compiled from a set of grammar rules that represent the rules used by the rule editor. A rule is a predicate of the form “<path><operator> [<operand>]. The <path> may represent a single value in the tree structured resource or a set of values. The <operand> may be of types boolean, string, integer, double, float or null. The model built by the model builder332is used to guide possible value in each of <path>, <operator> and <operand>. Within <path>, keyword search is allowed, with the results rank ordered of the best match, from left to right. The <operand> set is computed from <path>.

Once a rule is completed and validated, the rule may be sent for evaluation, for a defined period of time. Based on the evaluation, a result may be obtained. Rule evaluation engine340may be configured to receive the rule and retrieve data from one or more databases of the SDI108A, for the applicable time window. As one skilled in the art appreciates, one or more of the rules so created may be stored in a data store for future use. For example, a rules data store342may be provided to store the created rules. In one example, the rules data store342may be part of the dynamic policy generator330. In one example, the rules data store342may be part of the DIQ engine112. In one example, the rules data store342may be part of the security appliance110.

Having described various functions and features of the DP generator330, an example NI model built by the model builder332for cloud resource X is now described with reference toFIGS.3B,3C and3D. Now, referring toFIG.3B, during time period T1, the various attributes350of resource X are shown. As previously described, various attributes associated with resource X are retrieved from one or more data stores of the SDI108A. Each field of the attribute is evaluated for the value type and a model is created. For example, the NI model352is shown, with field name354and corresponding leaf value type356. In this example, the NI model is shown in a table form, with each row of the table corresponding to a field name and corresponding value type.

As an example, referring to row358, the field “vpcid” has a value type of “String”, as the value of “vpcid” is “vpc-abc123”. And, in row360, field “monitored” has a value type “Boolean”, as the value of “monitored” at time period T1 is “true”. In row362, the value type for field “groupName” is “UNKNOWN” at this time, as the value for “groupName” is “null”. Similarly, in row364, the value type for field “ipPermissions[*]” is “UNKNOWN” at this time, as the value for “ipPermissions” is known to be an empty array, with no associated data. Note that the field representing “ipPermissions” is normalized to a generic array path, indicated by the wildcard [*]. In summary, the NI model352as shown is representative of the resource X, at time period T1, with a few fields with defined value types and a few fields with “UNKNOWN” value type.

Now, referring toFIG.3C, various attributes350for resource X, for a time period T2 and corresponding model352is shown. We notice that additional rows have been added to the model350, based on detecting additional fields and their corresponding values. For example, referring to row366, a new field name “description” has been added with a value type “String”. Also, we notice that some of the previously unknown value types have been updated with a value type. For example, referring to row368, we notice that value type for field “groupName” has been updated as a “String”, based on a value of “local network” for the field “groupName”.

As one skilled in the art appreciates, the model352shown inFIG.3Ccan have more fields added to the model, along with corresponding value types. However, there are still some fields that are unknown, for example, as shown in row370. This process of reviewing the attributes of the resource X over a period of time and updating the model, based on additional information will eventually lead to the NI model352shown inFIG.3D, with all of the fields and their corresponding value types identified. At this time, the NI model352is considered to be complete.

As previously described, the information retrieval engine334generates one or more path documents in the retrieval system, for each of the resources. In one example, each path document may refer to data contained in a row of the NI model created by the model builder, with additional information fields. In one example, an address to a document may be indexed for easy access. Each path document in the retrieval system contains a unique path and all of its associated information such as cloud type, resource name, service name, set of applicable operators and suggested values. The cloud type, resource name and service name are derived from the mappings provided in the database schemas. The applicable operators are extracted from the normalized paths and the type of value stored against that path. Example path document created by the information retrieval engine for path “vpcid” is shown inFIG.3E.

Referring toFIG.3E, an example path document372created for row358of model352shown inFIG.3E. The path document372includes path (in this case, “vpcid”) and type (in this case, “string”, along with additional information fields like cloud type, service type, resource name and API name for the resource X.

As previously described, the rule authoring system336is configured to provide context sensitive help to a user, when a rule is being formed by a user. The rule authoring system336works in conjunction with the rule compiler338to provide context sensitive help to a user. In general, a rule follows one of these formats. One example format is <path><operator><operand>. An example of a rule according to this format is ‘path contains “foo”’. Another example of a rule according to this format is ‘path[*] size>0’. Another example format is <path><operator cum operand>. An example of a rule according to this format is ‘path is true’. Another example of a rule according to this format is ‘path[*] exists’. Now, an example context sensitive help provided to a user, based on the model352shown inFIG.3Dis described.

Referring back to the model352shown inFIG.3D, top level paths that a user may want to choose are first presented, without exposing lower level details. Based on the model352shown inFIG.3D, top level paths are “groupid’, “description”, “ownerid”, “vpcid”, “tags[*]”, “ipPermissions[*]”, “ipPermissionsEgress[*]” and “groupName”. Once a list of paths is presented to the user, the next level of recommendation depends on the path chosen by the user to build a conditional. If the path selected is already complete, pointing to a leaf node in the tree representing the data, the rule authoring system336would present only applicable operators. On the other hand, if the selected path is an entry to a subtree (out degree more than 1), then the system would present a set of path extensions as well as operators applicable at the intermediate node. An example path-operator recommendation table374for model352is shown inFIG.3F.

Referring toFIG.3E, table374, column376shows path chosen, column378shows leaf value type and column380shows sample recommendations, based on the leaf value type. For example, row382shows singular field like, “description”. And, row384shows partial path leading to multiple leaves, for example, fields like “tags[*]”. As one skilled in the art appreciates, an operand recommendation will be based on the type of leaf value. For example, if the leaf value is boolean, one of “true” or “false” is presented as an option to choose.

As the rule construction progresses, the existing string received from the query editor is sent to the rule complier338. If a rule is incomplete, the rule compiler338returns possible completion scenarios back to the rule authoring system336, which is presented to the user. A visual rule completion indicator may be presented to the user during the rule construction process. If the rule compiler338concludes that a rule is complete, the visual indicator may change a state to indicate completion. For example, a “X” mark may be displayed to the user when the rule is not complete and a “√” mark may be displayed when the rule is complete. Further, one or more colors may be used to further accentuate the rule completion indicator. Rule is considered complete when all the conditionals satisfy the underlying grammar. As one skilled in the art appreciates, the rule may be extended with additional conditionals beyond the completed state until next state of completion is reached.

In one example, the rule evaluation engine340retrieves the rules stored in the rules data store and based on the rule attributes, retrieves corresponding data from the SDI108A. In one example, the rule evaluation engine340minimizes the data needed for matching the rule attributes, by using a pattern defined by the antecedents contained in the rule. For example, only fields and paths matching the pattern are saved as a minimized version of the retrieved data, in the mindata data store344.

Referring toFIG.3G, an example minimization of data retrieved from the SDI for a resource is described. Table386shows retrieved data from the SDI, with column388showing field and column390showing corresponding value. For example, referring to row392, field “encrypted” is “false”. Compiled rule is shown in table394. The rule evaluation engine340reviews the retrieved data shown in table386against the compiled rule shown in table394. Based on the review of the rule, only two fields are relevant, namely “encrypted” and “ipAddresses[*].from port”. The rule evaluation engine340only retains rows corresponding to these two fields and stores an updated table396in the mindata data store344.

The rule evaluation engine340further uses an ordering of conditions in a constructed rule, based on the type of condition. Conditions that do not contain an operand, such as boolean or field existence checks are promoted or ordered to be in the front of the rule. Following this ordering, leaf level checks are added and finally, operators that deal with intermediate nodes in the tree, requiring computation of all decedents, are added at the end of the rule. One of the benefits of such an ordering of conditions is to enable a fast fail, stop computing as soon as a boolean predicate is no longer true, instead of computing all the conditions. As one skilled in the art appreciates, a substantial amount of time may be spent on evaluating predicates that involve intermediate nodes in the tree, due to the need for navigating to the leaves and checking the rules. Rule394shown inFIG.3Gis an example rule that has been ordered. For example, “encrypted is false” precedes “ipAddresses[*].fromPort” in the rule.

In one example, using the minimized data stored in the mindata datastore344, an inverted map between the minimized data and the ID of the data indicative of the primary key for retrieval of data from one or more databases of the SDI108A is created. This inverted map will, in some examples, eliminate redundant computation across duplicates, which may enable scaling and performance. The resulting inverted map is then evaluated against the rule filter.

FIG.3Hshows an example inverted map table3000. Column3002shows the minimized data and column3004shows primary key IDs that correspond to the minimized data. Now, referring to row3006, data corresponding to column3002shows minimized data field of “a” and “b” with their corresponding values. Further, data corresponding to column3004shows primary key IDs of ID1, ID2 and ID3. As previously described, ID1, ID2 and ID3 correspond to the primary keys associated with documents containing data field of “a” and “b”, with their corresponding values.

Now referring back to the table396shown inFIG.3G, in one example, data field “a” may correspond to “encrypted” and corresponding value for field “a” may correspond to “false”. Similarly, data field “b” may correspond to “ipAddresses[*].from port” and corresponding value for field “b” may correspond to “22”.

Join Across Multiple Resources:

In some examples, it may be beneficial to apply rules with variables that may be applicable to more than one resource. Rule evaluation engine340may be configured to perform this function. Example implementation will be described with reference toFIG.3I. In this example, there are two resources, resource X and resource Y. Table3100shows data retrieved from the SDI for resource X. Table3102shows data retrieved from the SDI for resource Y. Table3104shows the rule to be applied across resource X and resource Y. For example, the rule to be applied is X.vpcid equals Y.resourceID. First, the rows of data in table3100and3102are merged by a merger engine executed in the rule evaluation engine340. Then, the minimizer engine executed in the rule evaluation engine340generates a minimized data of the merged data, by retaining only fields and corresponding values that are applicable to the rule3104. The generated minimized data is shown in table3106, where column3108shows the field and column3110shows the corresponding value. The minimized data is stored in the mindata data store344by the rule evaluation engine340.

Dependency Directed Updates:

In one example, the rule evaluation engine340receives any updates to the data corresponding to a resource of the SDI. The update(s) to the resource is evaluated to determine if it is an addition, deletion or modification of a field. The fields that are updated are checked against the data stored in the mindata data store. If the mindata data store does not have a corresponding field, the update is ignored, as no rule currently present is using that field. If on the other hand, the mindata data store does have a corresponding field, the value for the field is updated. In one example, rules that use the updated field are checked for compliance, based on the updated value and an alert is generated if the rule requires generation of an alert.

For example, referring back toFIG.3Hand inverted map table3000, if the updates do not contain any changes to fields “a” and “b”, those changes are ignored. If on the other hand, if there is a change to one or more of fields “a” or “b”, corresponding primary keys ID1, ID2 and ID3 are used to retrieve associated documents and checked against corresponding rules for compliance, based on the changed value of “a” and/or “b” as applicable. Based on the compliance check, if there is a violation, appropriate alerts are generated.

Having described an example dynamic policy generation, example query to retrieve new or changes assets in a given period of time is described. Now, referring toFIG.4, operation of an example query to retrieve new or changed assets in a given period of time is described. The query interface314of the DIQ engine112receives a query402as shown inFIG.4. The query402is requesting the DIQ engine112to return any new or changed asset configurations for a given period of time, in this case from 2016-03-01T00:00:00Z to 2016-03-02T00:00:00Z. In other words, for a period of one day from Mar. 1, 2016.FIG.4also shows some of the asset data stored in the low latency data store308, corresponding to this time period. As an example, the query interface314may issue one or more queries to the low latency data store308to retrieve correspond stored data for the selected time period.

For example, table404shows an inventory table. The inventory table404in column406shows Id, column408shows type, column410shows first_seen and column412shows last_seen. Referring to row414, asset type ACL with an Id of “1” was first_seen (or deployed) at 2016-01-01T00:00:10Z and last_seen (or terminated) at 2016-03-01T23:30:00Z. Similarly, referring to row416, an asset type “Host” with an ID of “3” was first_seen (or deployed) at 2016-03-01T09:00:33Z and last_seen (or terminated) at 2016-03-01T09:02:00Z.

Next, table420shows an ACL rules table. The ACL rules table420in column422shows acl_id, column424shows time and column426shows corresponding permission for the specified ACL. Referring to row428, for acl_id of “1”, at time 2016-01-01T00:00:10Z, the permission granted was “inbound TCP*:* to *:80”. In other words, the ACL with an acl_id of “1” permitted access from any IP address (internal or external) to port80of any resource to which acl_id of “1” was assigned.

Now, referring to table430, an ACL attachment table is shown. The ACL attachment table430in column432shows resource_id, column434shows time, and column436shows acl_id. Referring to row438, we see that acl_id of “1” was attached to resource_id of “3” (which happens to be of the type “Host” based on inventory table404) at time 2016-03-01T09:00:33Z. In other words, Host with an id of “3” is permitted to receive inbound traffic from any IP address to its port80(based on row428of ACL rules table420).

Now, referring to table440, an interface attachment table is shown. The interface attachment table440in column442shows interface_id, column444shows time, column446shows attached_to and column448shows Ip. Now, referring to row450of table440and inventory table404, we notice that interface_id of “2” was attached to host “3” with Ip addresses of 10.10.0.21 and 93.184.216.34.

Now, referring to table452, an asset properties table is shown. The asset properties table452in column454shows Id, in column456shows time, in column458shows tag_key and in column460shows tag_value. Now, referring to row462, we notice that Id of “3” at time 2016:03-01T09:02:00Z had a tag_key of Id=1-1001. From inventory table404, we know that Id of “3” corresponds to the “Host”. Referring to row464, we notice that Id of “3” (“Host”) at time 2016-03-01T09:02:00Z had for a tag_key of “name”=“autotest-host”. Now, referring to row466, we notice that Id of “3” (host) at time 2016-03-01T09:02:00:00Z had for a tag_key of “env”=“Production_web”.

The query interface314, based on the query402and associated data in the inventory table404, ACL rules table420, ACL attachment table430and interface attachment table440returns a result as shown in result470. In other words, the result470indicates that Id of “3” (host) at time 2016-03-01T09:02:00Z had an interface Id of “2”, with an ACL Id of “1”.

In one example, the result470of the query402may be used to scan for possible compliance violation. This is described with reference toFIG.5. Referring toFIG.5, a policy table500is shown. The policy table500in column502shows rule_id, in column504shows asset_type and in column506shows applicable rule. For example, referring to row508, we see that rule_id of “1” applies to asset_type of “Host” and the rule is “if asset.tag(env)!=‘production_web’ then “No inbound network from internet”.

In one example, the policy compliance table may be stored in the low latency data store308. In one example, the policy compliance table may be stored in the SDI and retrieved by the security appliance112. The result470is fed to the policy compliance engine116, as described with reference toFIG.2. The policy compliance engine116retrieves the applicable rule information for the asset_type identified in the result from the policy compliance table500. The policy compliance engine116analyzes the data contained in the inventory table404, ACL rules table420, ACL attachment table430, interface attachment table430and asset properties table452as against the rule applicable to the asset_type “host”. As previously described with reference toFIG.4, and more specifically, row466of Asset properties table452, policy compliance engine116determines that the host with an Id of 3 and a tag_key of “env” has a tag_value of “Production_web”. So, the rule identified in row508of the policy table500is applicable to host with an Id of 3. Further, based on the result470, the policy compliance engine116checks the applicable ACL rules, in this case, for an ACL Id of “1”. Based on the review of the ACL rules table420for an ACL Id of 1, any inbound TCP traffic is permitted, as previously described with reference toFIG.4. This ACL permission is not permitted per policy rule identified in row508of the policy table500. Therefore, the policy compliance engine116concludes that there was a violation of policy rules during the deployment of host with a host ID of “3”. The policy compliance engine116generates a violation report detailing the violation to the notification engine120. The notification engine120sends an appropriate message to the user informing the violation. An example violation report510sent by the notification engine120is now described.

The violation report510may include one or more components. In one example, the violation report510may include a message512, a network query514and an event query516. In this example, the message512includes a human readable text as shown in block518. The network query516in this example, will be presented as a hyperlink, which when activated, submits a customized query to the query interface314of the DIQ engine112, to retrieve all related network flow. In this example, the network query is shown in block520. The event query516in this example, will be presented as a hyperlink, which when activated, submits a customized query to the query interface314of the DIQ engine112, to retrieve all applicable audit events associated with assets in question reported by the SDI during the applicable time period. In this example, the event query is shown in block522.

As part of processing queries provided by users or external processes and correctly address various data sources, the query interface314has to understand relationships between different data types and different asset types. In some examples, these relationships may be static relationships and expressed as static relationship rules. In some examples, these relationships may be dynamic relationships and expressed as dynamic relationship rules or inferred relationship rules. An example of context stitching by the security appliance110of this disclosure with static relationship rules will be described with reference toFIG.6.

Referring toFIGS.6-1and6-2, an example processing of a high level query using static relationship rules will now be explained.FIG.6-1shows various tables stored in the data stores of the DIQ engine112. For example, table404-1shows an inventory table. Inventory table404-1is similar to inventory table404, previously described with reference toFIG.4. Table420-1shows an ACL rules table. ACL rules table420-1is similar to ACL rules table420, previously described with reference toFIG.4. Table430-1shows an ACL attachment table. ACL attachment table430-1is similar to ACL attachment table430described with reference toFIG.4. Table440-1shows an interface attachment table. Interface attachment table440-1is similar to Interface attachment table440, previously described with reference toFIG.4. Table452-1shows an asset properties table. Asset properties table452-1is similar to asset properties table452previously described with reference toFIG.4.

Table602shows an example static relationship table602. Each of the rows of static relationship table602articulates one of the static relationship rules which can be read and understood by the query interface314. For example, referring to row604, one of the rules is “Host has an Interface where Interface.attached_to=Host.id”. Now, referring to row606of interface attachment table440-1, we see that interface_id of “2” is attached_to “3”. Here, based on the rule defined in row604of the static relationship table602, the number “3” refers to a “host.id”.

Similarly, referring to row608of the static relationship table602, the rule is “Interface has an IP attachment”. So, referring back to row606of interface attachment table440-1, we see that “interface_id” of “2” has IP attachment to IP addresses 10.10.0.21 and 93.184.216.34.

Table472shows an example network flow table. Network flow table472in column474shows time, column476shows protocol (proto), column478shows source IP address (srcip), column480shows destination IP address (dstip), column482shows destination port (dstport) and column484shows number of bytes transferred (bytes). Now, referring to row486of network flow table472, we notice that at time 2016-03-01-T09:01:10Z, using “tcp” protocol, an asset with a source IP address of 10.10.1.52 sent 3000 bytes of data to port “80” of another asset with a destination IP address of 10.10.0.21.

Now, referring toFIG.6-2, an example processing of a high level query using static relationship rules will now be explained. An example query608is received by the query interface314from a user or an external process. Query608is directed to retrieve network flow from a specific asset with an asset ID of “1-1001”, where the asset is either a source or a destination. In order to process the query608, the query interface314retrieves some relevant intermediate data, as shown in block610. For example, the query interface314issues one or more queries to retrieve intermediate data. For example, referring to row612of block610, the query interface314first retrieves ID value from the asset property table430-1, based on a tag_value of “1-1001” for the tag_key-“id”. Row614of the asset property table430-1matches this request and the corresponding ID value is 3.

Next, referring to row616of block610, the query interface314next retrieves the “type” for “id=3” from the inventory table404-1, which corresponds to data in row618of inventory table404-1. Based on the data in row618of inventory table404-1, the “type” for “id=3” is a “Host”.

Next, referring to row620of block610, the query interface314next retrieves the IP addresses from interface attachment table440-1, where “attached_to” value is equal to “3”. This corresponds to row606of interface attachment table440-1. Based on the data in row606of interface attachment table440-1, the IP addresses are 10.10.0.21 and 93.184.216.34.

Now, referring to block622, another query is issued by the query interface314to the network flow table472, to retrieve all network flow data where source IP address is 10.10.0.21 or 93.184.216.34 or destination IP address is 10.10.0.21 or 93.184.216.34. We notice that rows486,488and490of network flow table472has entries corresponding to IP addresses 10.10.0.21 and 93.184.216.34. Corresponding information is received as a response to the issued query.

In one example, the query interface314consolidates the received information in a table form. For example, the query interface314constructs a nodes table624and an edges table626using interrelated data from various data sources of the AIQ engine112. The nodes table624in column628shows node name, in column630shows IP address and in column632shows ACL used. For example, referring to row634of nodes table624, we notice that a node name of “Autotest-host” was assigned to IP addresses 10.10.0.21 and 93.184.216.34, with an assigned ACL of 1. As previously described, the host with a host ID of 3 was assigned IP addresses 10.10.0.21 and 93.184.216.34. Further, based on the asset properties table452-1, host with an id of “3” had a “name” of “autotest-host”. Further, host with an “id” of 3 had acl_id of 1 assigned per ACL attachment table430-1.

The edges table626in column636shows network flow from a node (From), column638shows network flow to a node (To) and column638shows number of bytes (bytes) transferred. For example, referring to row624, we notice that node 10.10.1.52 transferred 3000 bytes of data to node “Autotest-host”.

In some examples, information stored in the nodes table624and edges table626may be represented as a table on a display device of a user computer. In some examples, information stored in the nodes table624and edges table626may be sent to the visualization engine118, which may communicate with the rendering engine132to present the retrieved results in a graphical form. An example graph650displayed on a display device of the user computer is shown. In one example, the graph650may be a directed graph, showing the node name from the node table624, with directional lines connecting the nodes based on information from the edges table626. The directional lines represent the edges between the nodes. In one example, when a user hovers over a node, additional information related to the node may be displayed by a popup screen. For example, additional information stored in the nodes table624may be displayed. Information like the IP address and applicable ACL may be displayed. In one example, when a user hovers over a directional line connecting two nodes, additional information related to the edges may be displayed by a popup screen. For example, additional information stored in the edges table624may be displayed. Information like number of bytes transferred may be displayed.

In some examples, a dynamic (or inferred) relationship rules table may be created, based on observed events by the security appliance110. For example, a lead event, say an audit event may be succeeded by one or more additional events, for example, one or more audit events. The security appliance110, in some examples, the machine learning engine114of the security appliance110may evaluate a plurality of events and come up with one or more inferred relationships. Referring toFIGS.7-1and7-2an example query processed by the query interface314using inferred relationship rules.

Referring toFIG.7-1, various tables stored in the data stores of the DIQ engine112is shown. For example, table404-2shows an inventory table. Inventory table404-2is similar to inventory table404, previously described with reference toFIG.4. Table702shows a roles table. Roles table702in column704shows “Id” of a user or a process and in column706shows permission granted to the user or process (permission). For example, referring to row708of roles table702, user “Alice” is granted permission to “ListAssets, CreateHost, DeleteHost and EditAcl”. Table710shows an owners table. Owners table710in column712shows “resource_id”, in column714shows “time” and in column716shows “owner_id”. For example, referring to row718of owners table710, we notice that resource_id of “3” at time 2016-03-01T09:00:33Z had an owner_id of “AutoTestScript”.

Table720shows an audit event table. Audit event table720in column722shows “id”, in column724shows “time”, in column726shows “subject”, in column728shows “action” and in column730shows “object”. For example, referring to row731of audit event table720, we notice for “id” of “1”, at time 2016-03-01T09:00:33Z, subject was “AutoTestScript”, action was “CreateHost” and Object was “3”.

Table732shows an inferred relationship rules table. Inferred relationship rules table732shows inferred relationships based on evaluation of various events over time. For example, referring to row734of inferred relationship rules table732, we notice that “CreateHost” action is followed within 30 seconds by “AttachInterface” action with Subject=Root and AttachInterface. Objects contains CreateHost.Objects. Similarly, referring to row736of inferred relationship rules table732, we notice that “CreateHost” action is followed within 30 seconds by “AssignIP” with Subject=Root and prior AttachInterface.Objects contains CreateHost.Objects and AttachInterface.Objects contains “AssignIp.Objects.” These rules further assist the query interface314of the DIQ engine112to appropriately interpret a series of audit events and determine applicable assets related to the audit events. As an example, referring to audit events table720, we notice that action in event id of “1” is a “CreateHost”. Based on rule shown in row734of inferred relationship rules table732, event id of “2” is related to event id of “1” in the audit events table720. And, based on the rule shown in row736of inferred relationship rules table732, event id of “4” is related to event id of “1” in the audit events table720.

Now, referring toFIG.7-2, an example query processed by the query interface314using various tables described inFIG.7-1will be explained. An example query received by the query interface314is shown in block738. The query is to retrieve all audit events where object is “asset” with an “id” of 3 between a time period of 2016-03-01T09:00.33Z and 2016-03:01T09:12.00Z. Block740shows relationships between event 1 and event 2 and event 1 and event 3 of the audit events table720, based on the rules of the inferred relationship rules table732, as previously described.

Table742shows the audit events results of the query, showing selective events satisfying the query request. For example, column744shows time, column746shows “subject”, column748shows “action” and column750shows “detail”. As an example, referring to row752of the audit events results table742, we notice that at time “2016-03-01T09:00:33Z, “AutoTestScript” process executed an action “CreateHost”. In this example, the query interface314has further retrieved details of the action, from other tables of the DIQ engine, which is shown in the details column. For example, the asset properties table as previously described may be used to provide further details. For example, a host ID of “1-1001” was assigned in this audit event.

As one skilled in the art appreciates, ACLs allow input traffic to an asset. In some examples, it may be beneficial to know which ACLs are used and which ACLs are not used in a given time frame, by an asset. A dormant ACL may pose potential risk to an enterprise. By knowing the usage of the ACLs by an asset, one or more ACLs not used may be retired or deleted. Referring toFIG.8-1andFIG.8-2, determination of usage of ACLs and identification of dormant ACLs is explained.

Referring toFIG.8-1, various tables stored in the data stores of the DIQ engine112is shown. For example, table404-3shows an inventory table. Inventory table404-3is similar to inventory table404, previously described with reference toFIG.4. Table420-2shows an ACL rules table. ACL rules table420-2is similar to ACL rules table420, previously described with reference toFIG.4. Table430-2shows an ACL attachment table. ACL attachment table430-2is similar to ACL attachment table430, previously described with reference toFIG.4. Table440-2shows interface attachment table. Interface attachment table440-2is similar to interface attachment table440, previously described with reference toFIG.4. Table452-2shows an asset properties table. Asset properties table452-2is similar to asset property table452, previously described with reference toFIG.4. Table472-1shows a network flow table. Network flow table472-1is similar to network flow table472, previously described with reference toFIG.4.

Now, referring toFIG.8-2, an example query processed by the query interface314using various tables described inFIG.8-1will be explained. An example query received by the query interface314is shown in block802. The query is to retrieve ACLs used by network traffic where source or destination is “1-1001” and time between 2016-03-01-T09:00:00Z and 2016-03-01-T09:13:00Z.

The query interface314retrieves intermediate data, by using a query shown in block804. Intermediate data is IP related to asset with tag(‘id”)=“1-1001”. As previously described with reference to block610ofFIG.6-2, using the tables shown inFIG.8-1, the query interface314determines that IP addresses related to asset with tag(“di”)=“1-1001” are 10.10.0.21 and 93.184.216.34.

Having determined applicable IP addresses for the requested query, another query is initiated by the query interface314, as shown in block806, to retrieve network traffic where source IP address or destination IP address is either 10.10.0.21 or 93.184.216.34, during a time period between 2016-03-01-T09:00:00Z and 2016-03-01-T09:13:00Z. Query as shown in block806retrieves matching records from network flow table472-1, as shown in network results table808.

Having retrieved the matching records as shown in table808, the query interface314now issues another query as shown in block810. The query shown in block810reviews each row of the network results table808to evaluate each ACL attached to asset attached to an interface with IP address contained in the destination IP of the flow. The query will tag ACLs which would allow this flow. Results of this query is shown in ACL results table812. The ACL results table812has column814showing flow row number (flow row #) which corresponds to the row number of the network flow in network results table808. For example, flow row # of 1 corresponds to network flow shown in row816of network results table808. Column818of table812shows acl_id ad column820shows if the acl_id shown in column818would allow the flow identified in the corresponding flow row #.

Now, referring to row822of ACL results table812, we notice that this corresponds to flow row #1 (shown in row816of network results table808), with a destination IP address of 10.10.0.21 and destination port of 80. Based on the interface attachment table440-2, row824, resource ID of “3” has an IP address of 10.10.0.21. And, based on the ACL attachment table430-2, resource ID of “3” has ACL_id of “1” assigned to it. Further, based on ACL rules table420-2, ACL_id of “1” would permit inbound network flow to port80. Therefore, in ACL results table812, in row822, for “would_allow” column, a value of “true” is assigned.

Now, referring to row830of ACL results table, we notice that for a resource id=“3” corresponding acl_id of “7” is assigned (based on row830of ACL attachment table430-2). From row832of ACL rules table420-2, we notice that acl_id of “7” permits inbound traffic to port22. However, network flow in flow row #1 (row816of network results table808) is to port80. Therefore, in ACL results table812, in row830, for “would_allow” column, a value of “false” is assigned. Similarly, the query interface314analyzes all the network flows identified in the network results table808and generates corresponding rows of information in the ACL results table812.

The query interface314analyzes each of the rows of the ACL results table812and returns acl_id corresponding to rows with “would_allow” column marked as “true” as final result to the query shown in block802. In summary, the result to the query is shown in block832. As one skilled in the art appreciates, reviewing the ACL results table812also indicates that acl_id of “7” is not used by any of the network flows. So, in some examples, this information may be used to delete the corresponding ACL from the ACL rules table420-2.

Previously, with reference toFIG.5, an example policy compliance violation was determined by the security appliance110. In some examples, it may be beneficial to know if a compliance violation occurred in the past, due to a changed rule. This will be further explained with reference toFIG.9-1andFIG.9-2.

Referring toFIG.9-1, various tables stored in the data stores of the DIQ engine112is shown. For example, table404-4shows an inventory table. Inventory table404-4is similar to inventory table404, previously described with reference toFIG.4. Table420-3shows an ACL rules table. ACL rules table420-3is similar to ACL rules table420, previously described with reference toFIG.4. Table430-3shows an ACL attachment table. ACL attachment table430-3is similar to ACL attachment table430, previously described with reference toFIG.4. Table440-3shows interface attachment table. Interface attachment table440-3is similar to interface attachment table440, previously described with reference toFIG.4. Table452-3shows an asset properties table. Asset properties table452-3is similar to asset property table452, previously described with reference toFIG.4. Table500-1shows a policy table. Policy table500-1is similar to the policy table500, previously described with reference toFIG.5. Table720-1shows an audit event table. Audit event table720-1is similar to audit event table720, previously described with reference toFIG.7

Now, referring toFIG.9-2, an example query processed by the query interface314using various tables described inFIG.9-1will be explained. An example query received by the query interface314is shown in block902. The query is to scan for new or changed asset configurations from time 2016-01-01-T00:00:00Z to 2016-03-01-T00:00:00Z. Time period in this example is longer than the time period in example described with reference toFIG.5.

As previously described with reference toFIG.4andFIG.5, as per the policy table500-1, for asset_type “Host”, if asset.tag “env” is a “production_web”, then, no inbound traffic from internet is permitted. Id of “3” corresponds to a “Host” per inventory table404-4. Resource ID of “3” is attached to acl_id of “1” per ACL attachment table430-3. Per ACL rules table420-3, row904, for acl_id of 1, inbound traffic is permitted from the internet to port80. Therefore, Host with a host id of “3” is in violation of policy in policy table500-1. This violation occurred between the times of 2016-02-10T080:00:00Z and 2016-02-12T09:20:00Z.

Based on the analysis described above, the query interface314generates a violation report as shown in block906. The violation report shown in block906is similar to the violation report as shown in block510and described with reference toFIG.5. The query interface314issues a query as shown in block908, to retrieve applicable audit events, for the criteria identified in the violation report shown in block906. As one skilled in the art appreciates, time window used in the event query may be adjusted (or extended) appropriately to capture all applicable events before and after the violation. Corresponding audit events are retrieved from the audit event table720and presented as audit event results table910. Referring to audit event results table910, we notice that user “Alice” made the changes to ACL rules.

As previously described, the machine learning engine114of the security appliance110periodically evaluates various events and generates rules and profiles for various assets and users. In one example, various audit events from DIQ engine112are evaluated by the machine learning engine114and generates a baseline for activities for a user. Generated baseline for activities for a user may be advantageously used to detect deviations from the norm, which may in some examples indicate an abnormal or malicious activity. This will be further described with reference toFIG.10.

Now, referring toFIG.10, an example user baseline table1002is shown. User baseline table1002in column1004shows user, in column1006shows feature and in column1008shows value. For example, referring to row1010, user Alice generally logs-in using a pair of IP addresses shown in the “value” column. Referring to row1012, we notice that Alice's geography for logs-in are from US. Referring to row1014, Alice generally performs EditAcl action about 1.4 times a session and referring to row1016, Alice generally performs CreateHost action about 0.2 times a session.

Table720-2shows an example audit event table. Audit event table720-2is similar to audit event table720, previously described with reference toFIG.7. However, in the audit event table720-2, an additional column1018is shown. In column1018, source IP responsible for the action is also shown.

In block1020, the audit event stream is monitored by the security appliance110. For example, the machine learning engine114of the security appliance110may monitor the audit event stream from the DIQ engine112. The machine learning engine114compares the audit event stream (for example, as shown in rows of the audit event table720-2for any deviation from the profile described in the user baseline table1002for the specific user. If there is any excessive deviation in the profile described in the user baseline table1002for the specific user, for example, above a threshold value, the machine learning engine114triggers the generation of a violation report.

Based on the review of the audit events from the audit events table720-2, the machine learning engine114determines that there is an excessive deviation from the baseline for Alice, as shown in block1022. For example, reviewing the audit events table720-2row with ID of 5 we notice that Alice logged-in from a source IP that is different than those identified in the user baselines table1002. Next, reviewing rows6-10of the audit events table720-2, we notice that there were five “CreateHost” actions by Alice within a short period of time, during a given session. This is inconsistent with Alice's baseline as shown in row1016of user baselines table1002, which is about 0.2 per session. Further, in this example, the IP address of 155.133.82.159 indicates a geo location other than US.

Block1024shows an example violation report generated by the security appliance110, to indicate the deviation from the baseline, for user Alice. The violation report shown in block1024is similar to violation report510described with reference toFIG.5, for example, with a message portion, a network query portion and an event query portion.

Now, referring toFIG.11, an example automated remedial action that may be taken by the remediation engine122of the security appliance110is described. As previously described with reference toFIG.10, the machine learning engine114monitors the audit events for any deviation from the baseline. When a deviation is detected, as previously described, in one example, the machine learning engine114may issue a trigger to the remediation engine122and send the violation report as shown in block1104for further action.

In one example, a remediation configuration table1102may be provided in the security appliance110. In one example, the remediation configuration table1102may be provided in the remediation engine122. The remediation configuration table1102provides steps to be taken by the remediation engine122, based on the reported violation. For example, column1106shows the violation (On violation), column1108shows remedy parameters and column1110shows remedy action. Referring to row1112, we notice that for “abnormal console activity”, remedy parameters are to determine the applicable assets and remedy action is to log-in to the applicable SDI and detach the currently attached ACL for the applicable asset and re-attach a new ACL to the applicable asset, where the asset is no longer accessible from the internet. In other words, ACL for the applicable asset is changed to quarantine the asset. The quarantined asset is investigated further for additional corrective action, as applicable. In block1114, remedial action is performed, as defined in the applicable row of the remediation configuration table1102.

Now, referring toFIG.12, an example flow diagram1200is described. In block S1202, configuration and operational information related to a software defined infrastructure (SDI) is retrieved. For example, the security appliance110retrieves the configuration and operational information related to the SDI, for example, using a SDI API. In one example, the data ingestion and query engine112of the security appliance110retrieves the information from the SDI.

In block S1204, selective information is extracted from the retrieved configuration and operational information. For example, the security appliance selectively retrieves information related to asset configuration, audit events and network flow log information.

In block S1206, extracted selective information is stored in a plurality of data stores. For example, extracted selective information may be stored in a low latency data store308, a bulk data store310and the aggregated network flow data store312. In one example, the extracted selective information may be stored in a plurality of tables. For example, an inventory table404, a ACL rules table420, ACL attachment table430, interface attachment table440, asset properties table452, policy table500, static relationship rules table602, network flow table472, inferred relationship rules table732, roles table702, owners table710, audit events table720, and user baselines table1002.

In block S1208, selectively stored information is evaluated for compliance to a policy. For example, compliance of an asset to an applicable policy as defined in the policy table500is evaluated, as described with reference toFIG.5andFIGS.9-1and9-2.

In block S1210, a report is generated based on the evaluation. For example, a violation report may be generated, as described with reference toFIG.5andFIGS.9-1and9-2.

In block S1212, a corrective action is initiated based on the evaluation. In one example, the violation report is sent to a user for further review and action. In some examples, the security appliance110may initiate a corrective action, for example, as described with reference toFIG.10andFIG.11.

The embodiments disclosed herein can be implemented through at least one software a running on at least one hardware device and performing various functions of the security appliance. Various functions of the security appliance as described herein can be at least one of a hardware device, or a combination of hardware device and software module.

The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof, e.g., one processor and two FPGAs. The device may also include means which could be e.g., hardware means like e.g., an ASIC, or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means are at least one hardware means, and at least one software means. The method embodiments described herein could be implemented in pure hardware or partly in hardware and partly in software. Alternatively, the invention may be implemented on different hardware devices, e.g., using a plurality of CPUs.

The principles of the dynamic policy generator are described with reference to the data stores of the SDI. However, principles of the query generator described with reference to the dynamic policy generator may be applied to any data stored in a tree structure and a corresponding NI model may be generated for the data stored in a tree structure.

The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the claims as described herein.