Abstract: Techniques can be implemented to provide for antivirus scanning in clustered storage where not all nodes of the cluster are connected to an antivirus server. A first computing node of computing nodes of a computing cluster can determine a status of an antivirus server. The first computing node can send a first indication of the status of the antivirus server to a group management protocol service of the computing cluster. The group management protocol service can send a second indication of whether the first computing node is available for antivirus scanning to a job engine of the computing cluster. The job engine can distribute an antivirus job among the computing nodes based on whether the first computing node is available for the antivirus scanning.

Abstract: Disclosed is a cyber threat intelligence platform configured to: a) designate a virtual machine as an attacker machine; b) designate a virtual machine as a victim machine; c) receive cyberattack data representative of a cyberattack executed by the attacker machine against the victim machine; e) receive defense action data representative of a defense action executed by the victim machine against the cyberattack; f) mark a first point in time when the cyberattack is executed, and mark a second point in time when the defense action is initiated; g) compare the first point in time with the second point in time to ascertain an attack-defense time lapse as a performance measure for computer system threat management of cyberattacks or defense actions, and h) view or analyze cyberattack and defense actions for effectiveness, including perspectives derived from the relative timing of the actions as indicated on the time lapse.

Abstract: Examples described herein provide for a system that evaluates a security level of a network system. Additionally, examples described herein evaluate a security level of a network system in order to enable a determination of components that can be used to enhance the security level of the network system.

Abstract: In one embodiment, a device in a network receives an attack mitigation request regarding traffic in the network. The device causes an assessment of the traffic, in response to the attack mitigation request. The device determines that an attack detector associated with the attack mitigation request incorrectly assessed the traffic, based on the assessment of the traffic. The device causes an update to an attack detection model of the attack detector, in response to determining that the attack detector incorrectly assessed the traffic.

Abstract: An event query host can include an event processor configured to process an event stream indicating events that occurred on a computing device. The event processor can add representations of events to an event graph. If an event added to the event graph is a trigger event associated with a query, the event processor can also add an instance of the query to a query queue. The query queue can be sorted based on scheduled execution times of query instances. At a scheduled execution time of a query instance in the query queue, a query manager of the event query host can execute the query instance and attempt to find a corresponding pattern of one or more events in the event graph.

Abstract: A system, method, and computer-readable medium are disclosed for performing a type-dependent event deduplication operation. The type-dependent event deduplication operation comprising: receiving a stream of events, the stream of events comprising a plurality of events, each event of the plurality of events having an associated event type; determining an event type of the plurality of events; parsing the plurality of events based upon the associated event type, the parsing providing a plurality of parsed events; and, performing a type-dependent event deduplication operation on the plurality of parsed events, the type-dependent event deduplication operation deduplicating events based upon the event type.

Abstract: A control flow graph representing a plurality of controls is constructed, wherein each control comprises a measure taken to counter threats to an IT infrastructure. For each path through the control flow graph, a metric quantifying an efficacy of the controls along the path in countering the threats is calculated. A threat strength distribution for threats to the IT infrastructure is constructed. A visualization of an efficacy of a combination of the plurality of controls is generated, based on the metrics, the control flow graph, and the threat strength distribution. A weakness in the plurality of controls is identified, based on the visualization. The plurality of controls is modified based on the identifying.

Abstract: Mechanisms for defending a computing system from attack are provided. The mechanisms include: maintaining a round counter that tracks a round number for a local host; determining a location in a graph for each of a plurality of hosts including the local host; determining monitor hosts of the plurality of hosts that are monitoring the local host; determining monitoree hosts of the plurality of hosts that are being monitored by the local host; sending a message to each of the monitor hosts identifying a value of the round counter; forwarding a first set of heartbeat messages from previous monitoree hosts to the monitor hosts; attempting to receive messages from the monitoree hosts; determining whether any messages were not received from the monitoree hosts; and in response to determining that one or more messages were not received from the monitoree hosts, generating an alert.

Abstract: Systems, computer program products, and methods are described herein for generating responsive actions based on unauthorized access events associated with imitation networks. The present invention is configured to retrieve information associated with unauthorized access attempts associated with an imitation dataset; generate penetration test scenarios based on at least the types of unauthorized access attempts; initiate the penetration test scenarios on real datasets stored in data repositories within a network environment; determine automated network security responses to the penetration test scenarios; determine the unauthorized access attempts that were not successfully blocked and/or reported; determine actions to be executed in response to the unauthorized access attempts that were not successfully blocked and/or reported; and update the network security features with the actions.

Abstract: Disclosed embodiments relate to systems and methods for automatically detecting and addressing security risks in code segments. Techniques include accessing a plurality of code segments developed for execution in a network environment, automatically identifying a first code segment from the plurality of code segments for analysis, automatically performing a first code-level security risk assessment for the first code segment, and determining a first security risk level for the first code segment based on the application programming interface risk level. The first code-level security risk assessment may be performed based on at least one of an application programming interface risk level, an embedded credentials risk level, and a target resource risk level. Further techniques may include determining a second security risk level for a modified version of the first code segment; and enabling a comparison between the first security risk level and the second security risk level.

Abstract: An indication that a change associated with adjusting capacity to provide security services to network traffic in a network environment is received. In response to receiving the indication, a set of instructions for configuring at least one of: a network device and a security appliance is determined. As a result of applying the instructions, at least one of: an amount of network traffic provided by the network device to the security appliance will increase, or at least a portion of network traffic that would otherwise be provided by the network device to the security appliance will instead be provided to another security appliance. The set of instructions is transmitted.

Abstract: Various embodiments of the present invention set forth techniques for monitoring risk in a computing system. The technique includes creating one or more risk objects, where each risk object of the one or more risk objects has a corresponding stored risk definition, the stored risk definition associating the risk object with raw machine data pertaining to the risk object, the raw machine data reflecting activity in an information technology (IT) environment. The technique further includes receiving a selection of a first risk object included in the one or more risk objects and receiving a first risk definition that corresponds to the first risk object. The technique further includes performing a search of the raw machine data according to the first risk definition, wherein a risk is identified based on the search of the raw machine data and performing an action based on identifying the risk.

Abstract: The disclosure relates to detecting vulnerabilities in managed client devices. A system determines whether a vulnerability scan of a computing device is required to be performed. The system installs a vulnerability detection component in the computing device in response to determining that the vulnerability scan is required to be performed. The system requests the vulnerability detection component to perform the vulnerability scan of the computing device. The system transmits a result of the vulnerability scan to a remote management service for the computing device.

Abstract: A regularization unit standardizes similar expressions across a plurality of URIs in access logs of requests made to a plurality of web servers, thereby changing the URIs into regularized URIs. A calculation unit calculates, among the access logs that are from the same source, the relative frequency of certain access logs to all access logs, the certain access logs corresponding to requests made to different destinations for the same regularized URI and also corresponding to certain response codes. If the largest of all the relative frequencies calculated for the regularized URIs is at least a certain threshold, a determination unit determines the regularized URIs to be scanning targets.

Abstract: Techniques, methods and/or apparatuses are disclosed that enable prediction of cyber risks of assets of networks. Through the disclosed techniques, a cyber risk prediction model, which may be a form of a machine learning model, may be trained to predict cyber risks. The cyber risk model may be provided to a cyber risk predictor two predict cyber risks of an asset, without the need to scan the asset at a very deep scan level.

Abstract: The present disclosure relates to an IP-based security control method and a system thereof. According to the present disclosure, the method comprises: selecting a target IP address that is an IP address of a security control target; generating IP monitoring information by scanning a port of the target IP address; determining an IP risk level of the target IP address by using the IP monitoring information; and generating a security report including at least one of an IP list determined by a preset IP risk level and IP monitoring information of an IP included in the IP list, wherein the IP monitoring information includes at least one of an IP address of the target IP address, banner information, application information, security vulnerability information, a malicious code, and a similar domain.

Abstract: Implementations of the present disclosure include providing a graph that is representative of an enterprise network and includes nodes and edges, a set of nodes representing assets within the enterprise network, each edge representing a lateral movement path between assets, determining, for each asset, a contribution value indicating a contribution of an asset, determining lateral movements paths between a first asset and a second asset, providing a lateral movement path value representative of a difficulty in traversing a respective lateral movement path, identifying a set of remediations based on remediations defined for one or more vulnerabilities associated with issues identified for assets, each remediation mitigating a cyber-security risk within the enterprise network, and prioritizing the two or more remediations based on contribution values of assets, lateral movement path values of paths, and one of lateral movement complexity values of respective segments of paths and costs of respective remediation

Abstract: An automated security assessment service of a service provider network may identify, and notify a customer of, misconfigured VM instances that can be access (e.g., via the Internet). A scanner tool may call an automated reasoning service to identify any VM instances of a customer that can be accessed, and may receive information from the automated reasoning service that is usable to exchange packets with those identified instances. The scanner tool can use the information to send requests to the identified instances. After receiving responses from the identified instances, the scanner tool can store, in storage of a network-based storage service, and in association with a customer account of the customer, encrypted data about the results of the scan (e.g., any VM instances that are vulnerable to attackers), and this encrypted data is thereby accessible to the customer with proper decrypt permissions.

Abstract: Systems, methods, and software described herein provide for managing service level agreements (SLAs) for security incidents in a computing environment. In one example, an advisement system identifies a rule set for a security incident based on enrichment information obtained for the security incident, wherein the rule set is associated with action recommendations to be taken against the incident. The advisement system further identifies a default SLA for the security incident based on the rule set, and obtains environmental characteristics related to the security incident. Based on the environmental characteristics, the advisement system determines a modified SLA for the security incident.

Abstract: The present disclosure describes defending against an attack execution operation. According to one aspect of the subject matter described in this disclosure, a method for generating a domain-specific language (DSL) file is disclosed. The method may comprise determining, a framework based on an attack repository, determining a first primitive based on the framework, and determining a second primitive based on the framework. In one implementation, the first primitive and the second primitive are fundamental structures or constructs within a DSL. The method further comprises combining the first primitive and the second primitive into a DSL file. In one implementation, the DSL file is executed to defend against a first attack execution operation executed by a threat-actor.

Abstract: Aspects of the subject technology relate to determining a defense surface change command to be applied to a defense surface. An organizational threat profile is stored and a baseline exposure score for threats is generated. The baseline exposure score is weighted based on at least the organizational threat profile to generate a prioritized exposure score. A defense surface change command is generated based on at least the prioritized exposure score, which is transmitted to hardware or software components, and an updated prioritized exposure score for the one or more hardware or software components is generated.

Abstract: The present describes simulating a threat-actor executing an attack execution operation. According to one aspect of the subject matter described in this disclosure, a method for generating a domain-specific language (DSL) simulant is disclosed. The method may comprise determining, a framework based on an attack repository, determining a first primitive based on the framework, and determining a second primitive based on the framework. In one implementation, the first primitive and the second primitive are fundamental structures or constructs within a DSL. The method further comprises combining the first primitive and the second primitive into a DSL simulant. In one implementation, the DSL simulant is executed to simulate a threat-actor executing an attack execution operation.

Abstract: A system and method for cloud native virtual machine (VM) runtime protection. The method includes creating a normal behavior model for a cloud native VM by training a machine learning model using a training data set including training activities performed by the cloud native VM, the cloud native VM being configured to provide at least one service, wherein the normal behavior model defines at least one capability of each service based on a set of capabilities for respective known services stored within a library of service-to-capability mappings, wherein each capability of a service indicates a plurality of discrete behaviors required by the service; and monitoring an execution of the cloud native VM to detect a deviation from the normal behavior model, wherein the deviation is caused by at least one abnormal behavior of one of the services that is not among the discrete behaviors defined in capabilities for the service.

Abstract: The present technology provides a browser extension that can recognize downloadable objects on a webpage and provides functionality that makes it easier and more efficient to download the downloadable objects to a location in a content management system. For example the present technology can analyze a document object model of a webpage to find attributes indicating a URL is associated with a downloadable object.

Abstract: According to one embodiment, An apparatus for preprocessing a security log includes a field divider configured to divide a character string of a security log into a plurality of fields on the basis of a structure of the security log, an ASCII code converter configured to convert a character string included in each of the plurality of divided fields into ASCII codes, and a vector data generator configured to generate vector data for each of the plurality of divided fields using the converted ASCII codes.

Abstract: A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators.

Abstract: The present disclosure provides a system and method of providing a security service by means of a network operator management system in a security management system, the method including receiving a high-level first security policy from an I2NSF (interface to Network Security Functions) user; receiving an available security service from a developer's management system; creating a low-level second security policy corresponding to the first security policy on the basis of the security service; and transmitting a packet including the second security policy for setting the created second security policy to each of a plurality of NSFs (Network Security Function) to an NSF instance, wherein the network operator management system and the NSFs are respectively connect to an I2NSF NSF-laving interface, and the second security policy includes at least one or more of 1) blocking SNS access during business hours, 2) blocking a malicious VoIP (Voice over Internet Protocol) or a malicious VoCN (Voice over Cellular Network)

Abstract: Introduced here are computer programs and computer-implemented techniques for building, training, or otherwise developing models of the behavior of employees across more than one channel used for communication. These models can be stored in profiles that are associated with the employees. At a high level, these profiles allow behavior to be monitored across multiple channels so that deviations can be detected and then examined. Moreover, remediation may be performed if an account is determined to be compromised based on its recent activity.

Abstract: An approach to workflow management in response to a detected security incident in a computer system. The approach may include an inference driven response based on prior artifacts. The inference driven response may predict the condition of the system and the outcomes of actions in response to the security incident. The predictions made by the inference drive response may be based on a machine learning model. The inference driven response may pause or prevent scheduled actions of the system based on the predictions. The inference driven response may continue to monitor the system and dynamically update its predictions for the condition of the system. In response to the updated predictions, the inference driven response may cancel or execute the previously scheduled actions of the system.

Abstract: Embodiments of the disclosure describe systems and methods for selecting a first group of users, which is selected to receive simulated phishing emails as part of a simulated phishing campaign, and adding users to a second group of users based upon those selected users interacting with a simulated phishing email that is part of a simulated phishing campaign; tracking the completion of remediation training related to phishing emails by users in the second group of users and receiving one or more indications that the users in the second group of users have completed remedial training; and automatically adding users, who are members of the second user group, to the first user group, to a third user group, or to a predetermined user group responsive to the one or more indications that the users in the second group of users have completed remedial training.

Abstract: Described herein are systems, methods, and software to enhance incident response in an information technology (IT) environment. In one example, an incident service identifies a course of action to respond to an incident in the IT environment. The incident service further identifies a particular step in the course of action associated with a credential requirement based on traits associated with the particular step, and generates a credential request to obtain credentials to support the credential requirement.

Abstract: A method for operating at least one log-analytics detection platform for detecting security threats associated with a client network, comprising: obtaining, via a communication network, log files from a client network, each log file comprising a log record associated with a channel and including an outbound communications log; extracting a channel feature set for said channels from said log files, said channel feature set comprises data pertaining to an associated entity, at least one channel feature being behavior of communication over a channel; aggregating said channel associated features for each of the channels into a data repository; generating a risk factor characterized by an entity score for said least one entity associated with entities of said channels; and blocking of communication for said entity when said risk factory is indicative of said entity being a security threat.

Abstract: Technology for risk-based access to secrets utilizes risk metadata tailored to secrets. Secrets include passwords, security tokens, digital certificates, and other items used for identity authentication, authorization, signing, validation, and other cybersecurity processes. A secret's risk metadata may indicate which controls protect the secret, the deployment scope of the secret or the asset it secures, known exposures of the secret, whether the secret secures other secrets, the impact if the secret is misused, the secret's strength, characteristics of the asset the secret secures, the secret's risk history, and other characteristics of secrets that set them apart. Unlike secrets, typical user-generated digital assets like web pages, documents, image files, and so on have value on their own. An enhanced system distinguishes between secrets and non-secrets when modulating access, making it possible to automatically provide consistent, efficient, and effective risk-based control over access to secrets.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency. An example method disclosed herein includes retrieving a log file in a first file format, the log file containing feature occurrence data, generating a first unit operation based on the first file format to extract the feature occurrence data from the log file to a string, the first unit operation associated with a first metadata tag, generating second unit operations to identify respective features from the feature occurrence data, the second unit operations associated with respective second metadata tags, and generating a first sequence of the first metadata tag and the second metadata tags to create a first vector output file of the feature occurrence data.

Abstract: The present invention discloses system and method to perform automated red teaming in organizational network replacing conventional orchestration and playbooks. The method includes obtaining input data and exit criterion for an organization from data sources. Further, the method includes determining attack surface associated with the organization based on the obtained input data and the exit criterion. The method includes identifying attack frontiers for the attack surfaces. Further, the method includes prioritizing the attack frontiers. Additionally, the method includes simulating the attack frontiers at the attack surfaces based on the prioritization. Moreover, the method includes determining attack paths associated with the attack surface based on results of simulation. Also, the method includes learning attack patterns associated with the attack paths based on the results of execution.

Abstract: Methods are provided in which a computing device obtains, from one or more disparate data sources, inventory data of a plurality of network resources in a plurality of domains of an enterprise network. The inventory data includes configuration information of the enterprise network. The method further includes the computing device selecting one or more contextual insights that apply to the inventory data of the enterprise network from contextual information related to one or more networks and configuration of the one or more networks and generating one or more contextual guides specific to one or more affected network resources of the enterprise network based on the one or more contextual insights.

Abstract: The present disclosure relates to methods, systems, and computer program products for generating an asset remediation trend map used in remediating against an attack campaign. The method comprises receiving attack kill chain data. The attack kill chain data comprises steps for executing an attack campaign on one or more assets associated with a computing device. The method further comprises parsing the attack kill chain data to determine one or more attack execution operations for executing the attack campaign on the one or more assets associated with the computing device. The method determines based on the parsing, one or more remediation operations corresponding to the one or more attack execution operations. In addition, the method sequences the one or more remediation operations to form an asset remediation trend map. In one implementation, the asset remediation trend map indicates steps for remediating the attack campaign.

Abstract: A computer-implemented method, computer system, and computer program product for threat management. A set of features used by a machine learning model is collected by the computer system to determine a threat type for an access attempt when the access attempt is detected. A cluster is determined, by the machine learning model in the computer system, for the access attempt using the set of features, wherein the machine learning model implements clustering to determine the cluster for the access attempt, and wherein the cluster for the access attempt corresponds to the threat type for the access attempt. A set of actions is performed by the machine learning model in the computer system based on the threat type determined for the access attempt.

Abstract: A cloud-based service monitoring device includes a criteria database and an exceptions database. The criteria database includes predefined configuration criteria corresponding to approved operating parameters of each cloud-based service being monitored. The exceptions database includes predefined configuration exceptions such that, for a given instance, each configuration exception corresponds to a different instance-specific criteria than the associated configuration criteria for the cloud-based service. The monitoring device extracts configuration settings from instances of the cloud-based service and compares the settings to the configuration criteria of the cloud-based service. If a suspect setting is identified that does not satisfy the configuration criteria at the service level, the monitoring device compares the suspect setting to instance-specific criteria.

Abstract: Systems and methods for determining and displaying comparative platform-specific security vulnerabilities with respect to cloud-based computing platforms are disclosed. To compare platform-specific security vulnerabilities of cloud-based computing platforms, the system detects a user interaction at a webpage for a network operation. The system then determines a first set of computing aspects associated with a set of cloud-based computing platforms using response data received from a processing of the network operation. The system then identifies a second set of computing aspects associated with a comparative cloud-based computing system platform and determines an overall-computing aspect impact level for associated computing aspects of the second set of computing aspects.

Abstract: Security control governance can significantly thwart attacks from external data. Inline processing can reduce and limit attack surfaces and enforce validators preselected for applications. Processing and saving data can be controlled based on confirmation that an application has implemented requisite security controls to validate data. The applicability of such a technical improvement to system operations improves the technical operations of most any system with one or more applications that accept potential attack surface items, such as data, data fields, or data types, from “open” or uncontrolled sources.

Abstract: A method by a network device to restrict access to a management interface, where the management interface is defined by a data model, and where the network device is provided by an equipment provider to an equipment operator for use by the equipment operator. The method includes receiving a first request from a management system to perform a first management operation that involves accessing a module of the data model, where the first request specifies a security credential as a key for a security wrapper defined by the module, and where the security credential is supplied to the management system by the equipment provider and is inaccessible to the equipment operator, verifying whether the security credential specified by the first request is valid, and performing the first management operation in response to verifying that the security credential specified by the first request is valid.

Abstract: Examples of the present disclosure describe systems and methods for a behavioral threat detection engine. In examples, the behavioral threat detection engine manages execution of one or more virtual machines, wherein each virtual machine processes a rule in relation to a context. The behavioral threat detection engine uses any of a variety of techniques to identify when events occur. Accordingly, the behavioral threat detection engine provides event indications, in the form of event packets, to one or more virtual machines, such that corresponding rules are able to process the events accordingly. Eventually, a rule may make a determination as to the presence or absence of a behavior. As a result, execution of the associated virtual machine may be halted, thereby indicating to the behavioral threat detection engine that a determination has been made. Thus a behavioral threat detection engine employs a behavior-based approach to detecting malicious or potentially malicious behaviors.

Abstract: A differentially private security system communicatively coupled to a database storing restricted data receives a database query from a client. The database query includes a relation specifying a set of data in the database upon which to perform the query and privacy parameters associated with the query. The differentially private security system determines a worst-case privacy spend for the query based on the privacy parameters and the relation. The differentially private security system performs the query upon the set of data specified by the relation and decrements the determined worst-case privacy spend from a privacy budget associated with the client. The differentially private security system records the worst-case privacy spend and the query at a log and determines a privacy budget refund based on queries recorded in the log. The differentially private security system applies the determined privacy budget refund to the privacy budget associated with the client.

Abstract: Systems and methods for determining and displaying platform-specific end-to-end security vulnerabilities via a graphical user interface (GUI) are disclosed. To provide users with visual indications of vulnerable computing aspects associated with a computing platform, the system identifies computing aspects associated with a platform. The system then obtains from a security entity, security-vulnerability descriptions that are associated with the platform. Using the security-vulnerability descriptions, the system then determines threat levels for each security-vulnerability description and then, using the determined threat levels, determines a computing aspect impact level for each computing aspect associated with the platform. The system then generates for display on a GUI, a graphical layout comprising each computing aspect impact level for each computing aspect associated with the platform.

Abstract: Sharing of user data of customers of a first party with a third party is monitored. The data is presented to customers to enable transparency with respect to what data is provided to whom. Furthermore, remediation is promptly triggered in response to a third-party data breach. After breach detection, customers and data affected by the breach is determined. The type of remediation is determined based on the risk as determined based on the customers affected by the data involved.

Abstract: Methods, apparatus, and processor-readable storage media for protecting data based on a context of data movement operations are provided herein. An example computer-implemented method includes identifying a context of a data movement operation based at least in part on a source and an indicated destination of data associated with the data movement operation; applying one or more data protection policies to the data movement operation based at least in part on the identified context, wherein a given data protection policy comprises one or more indications of one or more content scanners that are configured to detect data belonging to one or more regulated data classes; and in response to detecting data associated with the data movement operation that belongs to at least one of the regulated data classes, performing one or more automated remedial actions associated with the at least one regulated data class.

Abstract: Techniques for detecting suspicious data object access requests indicative of potential insider threats are described. A suspicious access detection module (SADM) determines, based on access data describing a access requests issued on behalf of multiple users, groups of the users having similar patterns of accesses to resource groups, a set of the resource groups accessed by each of the user groups, and ones of the user groups that are to be considered nearby others of the user groups based on having a threshold amount of resource group access similarities. The SADM causes an alert to be generated responsive to a determination that a subsequent access request is suspicious because it accesses a data object of a resource group that is not within the set of accessed resource groups of the issuing user's user group, and because the resource group is not within the sets of accessed resource groups of any nearby user groups.

Abstract: Embodiments provide a method for validating a network host listed in a body of an email. The method includes: receiving, by the processer, the email; checking, by the processer, whether a threat analytics option is enabled. If the threat analytics option is enabled, the method further includes: copying, by the processer, an original body field of the email into a new body field; converting, by the processor, a text representation of the network host into a link in the new body field; modifying, by the processer, a text style of the link in the new body field according to a threat analytics result from a threat analytics service; and displaying, by the processer, the email with the new body field, wherein the email includes the link having a modified text style.

Abstract: A system is provided for generating computing network segmentation and isolation schemes using dynamic and shifting classification of assets. In particular, the system may comprise various components that may identify and classify assets (e.g., computing devices) within a network, network topology, and vectors that may compromise one or more assets. The system may further comprise a component for mitigating and rectifying the effects of such vectors. Each asset within the network may be assigned a classification which may be dynamically modified and/or shifted by the system based on changing requirements and/or environments. In this way, the system may provide a more comprehensive way to protect the integrity and security of computing devices and/or electronic data.