Abstract: A method for setting a security level of wireless sensors communicating with a switch. The method includes in a security device linked to the switch: collecting data frames sent from the wireless sensors to the switch and creating a dataset containing the collected data frames; identifying patterns associated with the wireless sensors from the collected data frames; introducing simulated traffic anomalies in the dataset with respect to the traffic patterns; randomizing the dataset and dividing the randomized dataset into a training dataset and a testing dataset; training, using the training dataset, a machine learning model configured for detecting traffic anomalies, and validating the machine learning model; detecting a traffic anomaly for a wireless sensor by analyzing current data frames and using the validated machine learning model; triggering a security alert based on the detected traffic anomaly; and adapting a security level for the wireless sensor based on the security alert.

Abstract: Polymorphic non-attributable processes and architectures to monitor threat domains (e.g., pharming or phishing websites) are disclosed. Obfuscated requests may be generated by control servers to be blended in with normal traffic sent over cloud networks with randomized exit nodes or with normal traffic sent through an anonymization network. Requests may be sent at randomized intervals or time periods determined algorithmically. The requests are obfuscated in order to mask the origination information and location so that the threat actor does not detect that the website is being monitored. User agents may be spoofed and requests may present as if they originated from residential IP addresses. Automatic real-time monitoring can be provided to determine when sites resolve and are addressable. Fingerprint information, screenshots, security certificate, and other threat domain data can be captured. Request responses can be scanned for threat indicia.

Abstract: A data dimensionality reduction method includes: a step of dimensionally reducing a group of data from a high-dimensional space to a low-dimensional space using a distance function that defines a distance between any two vectors in the high-dimensional space; a step of dividing the dimensionally-reduced low-dimensional space into multiple subspaces; an analysis step of performing a regression analysis using a regression model based on at least one belonging data for each divided subspace; and a step of updating p first parameters included in the distance function based on results of the regression analysis in the multiple subspaces.

Abstract: A method for data-flow analysis includes constructing a data-flow graph for a computing system that runs multiple software applications. The data-flow graph includes (i) vertices representing data locations in the computing system, and (ii) edges representing data movements performed by the software applications between the data locations. One or more multi-hop paths are identified in the data-flow graph, each multi-hop path including a sequence of two or more edges that represents multi-hop movement of data in the computing system. One or more of the identified multi-hop paths are acted upon.

Abstract: Normalizing external application data is disclosed, including: receiving external application data associated with an external application; determining normalized metadata based at least in part on inferring from the external application data; and using the normalized metadata to monitor activities at the external application.

Abstract: A system of monitoring a user behavior for abnormalities compared to a group behavior includes a processor configured to implement instructions for a user to group behavior signature monitor (UGBSM) with at least one user, as a monitored user, and a group of one or more users, as baseline users, to access to certain characteristics of the monitored user and certain characteristics of the baseline users, calculate a user behavioral signature of the monitored user, calculate a group behavioral signature of the baseline users, calculate a degree of variance (DoV) between the user behavioral signature of the monitored user and the group behavioral signature of baseline users, and compare the calculated DoV to a variance threshold to determine whether the user behavioral signature of the monitored user is similar or is different from the group behavioral signature of the baseline users.

Abstract: Various examples are directed to systems and methods for installing a plugin to a cloud-implemented database management application. A shared container file system may be initiated at a cloud environment. A plugin container image may be accessed, where the plugin container image comprises plugin payload data describing a first plugin to the database management application and a copy executable. A plugin container may be started at the cloud environment, where the plugin container is based at least in part on the plugin container image. The plugin container may be mounted to the shared container file system. The copy executable may be executed to copy the plugin payload data to the shared container file system. The first plugin may be installed to a database management application instance executing at the cloud environment.

Abstract: Methods, systems, and devices for wireless communications are described. A first device may receive signaling associated with a traffic class from a second device. The first device may determine that the traffic class is included in a set of known traffic classes based on a set of features associated with the signaling. In response to determining that the traffic class is included in the set of known traffic classes, the first device may use a machine learning model to obtain a prediction of an application associated with the signaling. The prediction may be based on the set of features. The machine learning model may be trained at the first device or the second device. The first device may receive information associated with the machine learning model from the second device.

Abstract: The present invention provides an integrated, context-aware, security system that provides an adaptive endpoint security agent architecture model for a continuously monitoring and recording activity across an enterprise, specifically monitoring activity on endpoints, and subsequently detecting and blocking any malicious processes that may otherwise invade the enterprise and cause issues. The endpoint security agent architecture exposes a well-defined, public interface to the event data generated by the endpoint security agent in the form of a custom programming language by which a user can define the logic that the endpoint security agent executes in response to event data to perform detection of and response to suspicious activity.

Abstract: Disclosed herein are systems, methods, and software for managing bot detection in a content delivery network (CDN). In one implementation, a cache node in a CDN may obtain a content request without a valid token for content not cached on the cache node and, in response to the content request, generate a synthetic response for the content request, wherein the synthetic response comprises a request for additional information from the end user device associated with the content request. The cache node further may obtain a response from the end user device and determine whether to satisfy the request based on whether the response from the end user device indicates that it is a bot.

Abstract: An analysis engine receives data characterizing a prompt for ingestion by a generative artificial intelligence (GenAI) model. The analysis engine, using a prompt injection classifier determines whether the prompt comprises or is indicative of malicious content or otherwise elicits malicious actions. Data characterizing the determination is provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: Generally discussed herein are devices, systems, and methods for improving cloud resource security. A method can include obtaining a cloud resource management log that details actions performed by users of cloud resources in a cloud portal, the actions including entries comprising at least two of a user identification (ID) of a user of the users, an operation of operations performed on the cloud resource, a uniform resource identifier (URI) of a cloud resource of the cloud resources that is a target of the operation, or a time the operation was performed. The method can include determining a respective score for each action in the cloud resource management log, comparing the respective score to a specified criterion, and providing an indication of anomalous action in response to determining the respective score satisfies the specified criterion.

Abstract: The present disclosure relates to a system and method of automatically updating the set of security controls in the production environment using AI based on historical data generated in the test management system TMS during the system's testing in the testing environment including information about its elements, their properties, testing environment, its characteristics, and security controls with their settings. Once the AI has sufficient historical data from a testing environment, every time a change is detected to the system in the production environment, its elements, their properties, or at least one characteristic of the production environment, the AI system makes a recommendation to update the set of security controls in the production environment.

Abstract: A method, computer program product, and computing system for processing event data associated with a plurality of known operational impact events on a business service and operational data associated with the business service using a supervised machine learning model conditioned on an operational impact parameter associated with the business service. A detection threshold is generated using the supervised machine learning model.

Abstract: A cloud-based network security system (NSS) is described. The NSS uses a sandbox to safely detonate and extract information about a document and uses machine learning algorithms to analyze the information to predict whether the document contains malicious software. Specifically, during the detonation, static and dynamic information about the document is captured in the sandbox as well as character strings from images in the document. The dynamic information (and sometimes the static information) is input to an AI or machine learning model trained to provide an output indicating a prediction of whether the document contains malware. The character strings are compared with a batch of phishing keywords to generate a heuristic score. A validation engine combines the output from the AI or machine learning model and the heuristic score to classify the document as malicious or clean. Security policies can then be applied based on the classification.

Abstract: A computer-implemented method includes processing input packets; generating indexed logs, packets of network traffic, and system monitoring information; generating analytics or visualizations; and transmitting the analytics or the visualizations. A computing system includes a processor, a network interface controller; and a memory including instructions that, when executed cause the system to: process input packets; ingest system monitoring information; generate indexed logs, packets of the network traffic, and system monitoring information; generate analytics or visualizations; and transmit the analytics or the visualizations. A non-transitory computer readable medium includes computer-executable instructions that when executed, cause a computer to: process input packets; ingest system monitoring information; generate indexed logs, packets of the network traffic, and system monitoring information; generate analytics or visualizations; and transmit the analytics or the visualizations.

Abstract: Methods, storage systems and computer program products implement embodiments of the present invention for data access that include identifying a set of tables in a database to be accessed by an application, and identifying first and second application programming interface (API) calls having different, respective access properties for accessing records in the tables via an API. Respective counts of the records in the tables are computed by a processor, and the set of tables are partitioned into first and second subsets responsively to the respective counts. The records in the first subset of the tables are accessed by having the application convey the first API call to the API, and the records in the second subset of the tables are accessed by having the application convey the second API call to the API.

Abstract: There is provided a system and a computerized method of remediating one or more operations linked to a given program running in an operating system, the method comprising: querying a stateful model to retrieve a group of entities related to the given program; terminating at least a sub set of the group of entities related to the given program; generating a remediation plan including one or more operations linked to the given program, the one or more operations being retrieved based on the group in the stateful model; and executing the remediation plan by undoing at least part of the one or more operations linked to the given program thereby restoring state of the operating system to a state prior to the given program being executed. There is further provided a computerized method of detecting malicious code related to a program in an operating system in a live environment.

Abstract: Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.

Abstract: Methods and systems for detecting attempted manipulation of a machine learning model via explanation poisoning are provided. The method includes: computing explanations based on outputs of the model that include information that relates to features that affect the output of the model with respect to the first data point; assigning labels to the explanations based on the features; generating an explanation ensemble that resides in an N-dimensional space, N being equal to a number of assigned labels plus one; determining a region within the N-dimensional space for which a subsequent introduction of data causes a subsequent explanation that does not relate to the features; and when the additional data is introduced to the determined region, generating an alert message for notifying a user that a likelihood of adverse manipulation of the model is high based on the additional data.

Abstract: A method, computer program product, and computer system for receiving, by a computing device, a design scenario for execution on one or more internet based platforms. Seed data may be injected into the one or more internet based platforms during execution based upon, at least in part, the design scenario. Performance of the scenario may be monitored. A report may be generated based upon, at least in part, the performance.

Abstract: Method for detecting the linear extraction of information in a processor using an instruction register for storing an instruction including an operation code. The method includes monitoring the instructions successively stored in the instruction register including decoding the operation codes, determining the number of consecutive operation codes encoding incremental branches, and generating a detection signal if the number is greater than or equal to a detection threshold.

Abstract: Methods, systems, and apparatus are disclosed for an Artificial Intelligence based cyber security system. An Artificial Intelligence based cyber analyst can make use of a data structure containing multiple tags to assist in creating a consistent, expanding modeling of an ongoing cyber incident. The Artificial Intelligence based cyber analyst can make use of a cyber incident graph database when rendering that incident to an end user. The Artificial Intelligence based cyber analyst can also be used as a mechanism to evaluate the quality of the alerts coming from 3rd parties' security tools both when the system being protected by the cyber security appliance is not actually under attack by a cyber threat as well as during an attack by a cyber threat.

Abstract: A method for mitigating the effects of malware is provided. The method includes determining a compressibility of a portion of data, determining a data corruption condition is satisfied based on the determined compressibility, and modifying a retention policy for retention of stored snapshots associated with the portion of data based on the satisfaction of the data corruption condition. The modifying of the retention policy includes generating a first snapshot associated with the portion of the data, prior to writing cached data associated with the portion of the data, writing the cached data associated with the portion of the data, and generating a second snapshot associated with the portion of the data, responsive to the deletion.

Abstract: An example method for monitoring volume dependencies for security threats comprises: detecting a request to perform an operation with respect to a volume included in a plurality of volumes included in a storage system; determining, based on a dependency mapping that specifies dependencies between the plurality of volumes, that performance of the operation would affect a dependency between the volume and one or more other volumes included in the plurality of volumes; and determining, based on the determining that the performance of the operation would affect the dependency between the volume and the one or more other volumes, that the request is possibly associated with a security threat against data stored by the storage system.

Abstract: A computer-implemented method, computer program product and computing system for: obtaining hardware performance information concerning hardware deployed within a computing platform; obtaining platform performance information concerning the operation of the computing platform; obtaining application performance information concerning one or more applications deployed within the computing platform; and generating a holistic platform report concerning the computing platform based, at least in part, upon the hardware performance information, the platform performance information and the application performance information.

Abstract: The present disclosure relates to a method, a device, and a program product for managing a computing resource in a storage system. In one method, a processing request for processing a task using a computing resource is received. A length of time required for processing the task is acquired based on a usage state of the computing resource. A workload of the computing resource for processing a future data access request for the storage system within a future time period is determined based on a load model of the computing resource and a current workload of the computing resource. The load model describes an association relationship between a previous load and a subsequent load of the computing resource for processing a historical data access request for the storage system. A target time period matching the length of time is selected from the future time period based on the workload for processing the task. A corresponding device and a corresponding computer program product are provided.

Abstract: Systems, methods, and computer-readable media for performing threat remediation through a switch fabric of a virtualized network environment. Data traffic passing into a virtualized network environment including a plurality of virtual machines running on a switch fabric is monitored. A network threat introduced through at a least a portion of the data traffic is identified at the switch fabric. One or more remedial measures are performed in the network environment based on the identification of the network threat in the virtualized network environment.

Abstract: The present application discloses a method, system, and computer system for detecting parked domains. The method includes obtaining, by one or more processors, a set of webpages corresponding to a plurality of domains, extracting a plurality of features based on the set of webpages, detecting parked domains based on the plurality of features using a machine learning model, and periodically applying automatic signature generation to detect a new pattern of parked domains without retraining the machine learning model.

Abstract: Implementations include a computer-implemented method for mitigating cyber security risk of an enterprise network, the method comprising: receiving an analytical attack graph (AAG) representing paths within the enterprise network with respect to at least one target asset, the AAG defining a digital twin of the enterprise network and comprising a set of rule nodes, each rule node representing an attack tactic that can be used to move along a path of the AAG; integrating the AAG with a knowledge graph comprising a set of asset nodes, each asset node representing a digital asset that can be affected by one or more of the attack tactics; determining, based on integrating the AAG with the knowledge graph, a plurality of security controls, each security control having an assigned priority value; and selectively implementing the security controls in the enterprise network based on the assigned priority values of the security controls.

Abstract: A method for managing an attack surface is provided. The method comprises obtaining network traffic logs for the domain, correlating the logs to threats, mapping a flow of network traffic between malicious indicators and host identifiers, determining an exposed set of host identifiers, determining host attributes and indicator attributes of hosts identified in the exposed set, providing the exposed set and the attributes as input to a prioritization model, receiving prioritization scores as output from the prioritization model, and generating a prioritized attack surface data structure based on the scores. An interface is configured to modify a display based on the prioritized attack surface data structure.

Abstract: A method for handling rogue devices in a wireless communication network includes: detecting an interactive behavior between a user equipment and a network-side device in the wireless communication network; determining whether the user equipment is a rogue device according to the interactive behavior; and transmitting identification information of the determined rogue device to the network-side device, for the network-side device to perform blocking processing on the rogue device.

Abstract: In accordance with example embodiments as described herein there is at least an apparatus and method to perform transmitting packet data convergence protocol data unit duplicates over two or more carriers; receiving an indication that indicates packet data convergence protocol data unit has been correctly transmitted via one of two or more carriers; and upon receiving the indication, instructing to discard other packet data convergence protocol duplicates over carriers other than the one of two or more carriers with a successful delivery.

Abstract: Versions of an application program are evaluated to protect a customer from a supply chain attack. The versions of the application program are executed in to identify behaviors exhibited by the versions of the application program, each of the behaviors including activities that perform computer operations. A behavior change is detected by identifying a behavior that is not common to the versions of the application program.

Abstract: A non-transitory processor-readable medium stores code representing instructions to be executed by one or more processors, and the instructions include code to cause the one or more processors to (1) receive executable binary code and a specification that defines a constraint and (2) generate a predicate set. The code also causes the one or more processors to identify an argument of a function to be called by the executable binary code, the argument identified based on a map. A constrained predicate set is generated based on the predicate set and the machine-readable specification and, using solver software, the code causes the one or more processors to determine that the argument satisfies the constraint based on the constrained predicate set. A signal that indicates that the executable binary code is associated with a predetermined software action is generated in response to determining the argument satisfies the constraint.

Abstract: A cyber threat defense system can incorporate data from a Software-as-a-Service (SaaS) application hosted by a third-party operator platform to identify cyber threats related to that SaaS application. The cyber threat defense module can have a SaaS module to collect third-party event data from the third-party operator platform. The cyber threat defense system can have a comparison module to compare third-party event data for a network entity to at least one machine-learning model of a network entity using a normal behavior benchmark to spot behavior deviating from normal benign behavior. The comparison module can identify whether the network entity is in a breach state. The cyber threat defense system can have a cyber threat module to identify whether the breach state and a chain of relevant behavioral parameters correspond to a cyber threat. An autonomous response module can execute an autonomous response in response to the cyber threat.

Abstract: A fraud prevention system that includes a fraud prevention server including an electronic processor and a memory. The memory includes an online application origination (OAO) service. When executing the OAO service, the electronic processor is configured to determine whether the OAO service is enabled and whether a website configuration includes a list of multi-page placements for an online application, determine that input data needs to be stored in the memory and combined into multi-page input data, determine a fraud risk score of the online application based on the multi-page input data and an online application origination (OAO) model that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device, and control a client server to approve, hold, or deny the online application based on the fraud risk score that is determined.

Abstract: Computer implemented systems and methods for performing electromotive force analysis of a storage device that include a storage device, an Artificial Intelligence Co-processor (AI-Coprocessor) chipset, a thin coil inductor positioned in proximity to a portion of the surface of the storage device for capturing data from electro motive radia generated by the storage device, an analog-to-digital-converter, and at least one probe for communicating the captured data to an analog-to-digital converter. The data is captured by the thin coil inductor and communicated to the analog-to-digital-converter via the at least one probe and the analog-to-digital-converter digitizes the voltage level of the captured data and communicates the results of the digitization and amplification to the Ai-Coprocessor. The Ai-Coprocessor chipset performs analysis of the data to detect any anomalies in the operation of the storage device and outputs those result for further processing.

Abstract: Blockchain-based workflow management for patient samples. In an embodiment, for each of a plurality of first devices, a request for a unique sample identifier is received from the first device, and, in response to the request, the unique sample identifier is generated and sent to the first device. Then, for each of a plurality of second devices and for each of a plurality of interactions, interaction information is received from the second device, a transaction is generated from the interaction information, and the transaction is recorded in an immutable ledger. The interaction information may comprise a unique sample identifier, a user identifier, a location, an event type, and a timestamp.

Abstract: A computer-implemented method, computer program product and computing system for receiving a plurality of detection events concerning a plurality of security events occurring on multiple security-relevant subsystems within one or more computing platforms; storing the plurality of detection events to form an event repository; and processing the event repository using a machine learning model to identify attack patterns defined within the plurality of detection events stored within the event repository, thus defining one or more identified attack patterns.

Abstract: A system and method for network cybersecurity analysis that uses user and entity behavioral analysis combined with network topology information to provide improved cybersecurity. The system and method involve gathering network entity information, establishing baseline behaviors for each entity, and monitoring each entity for behavioral anomalies that might indicate cybersecurity concerns. Further, the system and method involve incorporating network topology information into the analysis by generating a model of the network, annotating the model with risk and criticality information for each entity in the model and with a vulnerability level between entities, and using the model to evaluate cybersecurity risks to the network. Risks and vulnerabilities associated with user entities may be represented, in part or in whole, by the behavioral analyses and monitoring of those user entities.

Abstract: An apparatus includes a memory that stores instructions; and a processing unit that executes the instructions to identify a created process, to receive a notification of a first event for an ancestor process and a notification for a second event for the created process, the notification of the first event indicating a first ActivityID and a first ID, the notification of the second event indicating a second ActivityID and a second ID, the first ID being different from the second ID, to perform a first determination that the created process was created by a component object model (COM) call, at least in part based on the second ID, and to perform a second determination that the ancestor process indirectly created the created process, at least in part based on the first and second ActivityIDs and the first determination.

Abstract: Disclosed is an approach for generating detection signatures based on analysis of a software representation of what is possible in a computer network based on network configuration data and network policy data. In some embodiments, the process includes maintaining a plurality of detection signature templates, generation of detection signatures (detection signature instances) using respective detection signature templates that are selected based on the analysis of the software representation. In some embodiments, detection signatures templates are of different type and may be deployed at different locations based on their respective type(s), such as at source, destination.

Abstract: An apparatus may be a UE configured to receive, from a network entity associated with a machine learning procedure, a first indication that a first set of data elements transmitted by the wireless device at a first time is categorized as misinformation and that, based on the categorization of the first set of data elements as misinformation, the network entity will temporarily exclude data from the wireless device from propagation as input for a subsequent machine learning procedure. The apparatus may further be configured to receive a second indication of a set of criteria for requesting a reevaluation of the categorization and transmit, based on meeting one or more criteria in the set of criteria, a second set of data elements to the network entity at a second time.

Abstract: The present disclosure provides a cleaning and recovery method and device for a heterogeneous executor in a mimic switch, and a mimic switch, the method includes: a mimic scheduler determining a designated heterogeneous executor that needs to be cleaned, marking the designated heterogeneous executor that needs to be cleaned as in a cleaning state, and sending a cleaning instruction to the designated heterogeneous executor; the designated heterogeneous executor sending a normal protocol negotiation message to the mimic scheduler to try to interact with the mimic scheduler; the mimic scheduler receiving the protocol negotiation message and detecting whether the designated heterogeneous executor is in the cleaning state; if in the cleaning state, the mimic scheduler constructing a training message, and sending the training message to the designated heterogeneous executor for protocol training; repeating sending and processing the protocol negotiation message until the designated heterogeneous executor is in a no

Abstract: Responsive to matching a site prefix to IPv6 network traffic from clients, the traffic as intended, and responsive to not matching the site prefix, classifying the corresponding traffic as unintended. An initial rate of packet occurrence and predict load caused by intended traffic and predicting load caused by unintended traffic is calculated, based on an initial rate of packet occurrence. The predicted traffic loads are fed back by configuring behavior of network modules according to the predictions of intended traffic load and unintended traffic load. Packet processing traffic at the network modules is based on traffic classification from the outcome of the AI-neuron.

Abstract: A secure localization method for multi-mobile robots system based on network communication includes: Step 1, establishing a nonlinear dynamic model of a multi-mobile robots system based on network communication; Step 2, designing a secure estimator for the nonlinear dynamic model; Step 3, calculating an upper bound on a one-step prediction error covariance matrix ?i,k+1|k for each mobile robot in the network communication; Step 4, based on ?i,k+1|k, calculating an estimator gain matrix Ki,k+1 for each mobile robot in the network communication; Step 5, substituting the estimator gain matrix Ki,k+1 calculated in Step 4 into Step 2 to obtain a state estimation {circumflex over (x)}i,k+1|k+1 at time k+1; determining whether k+1 reaches a total duration M, that is, if k+1<M, performing Step 6, and if k+1=M, ending; and Step 6, based on Ki,k+1, calculating an upper bound on an estimation error covariance matrix ?i,k+1|k+1 of each mobile robot; let k=k+1, and performing Step 2 until k+1=M is satisfied.

Abstract: A system, method, and apparatus for improving communication between mobile or wearable devices and access points of the wireless land area network. The method includes sensing at a client station a service set identifier for a wireless access network. The method also includes determining a subset of channels within the wireless access network by accessing information stored in a database, and determining a scan time interval associated with the service set identifier by accessing the information stored in the database. In addition, the method includes transmitting a probe request through the subset of channels to an access point located within the wireless access network and receiving a probe response at the client station from the access point during the scan time interval. Further, the method includes identifying location of the client station based on the probe response.

Abstract: Techniques for data classification using clustering. A method includes replacing a plurality of portions of metadata for a plurality of data objects with a plurality of replacement characters in order to generate a plurality of replaced strings; clustering the plurality of data objects into a plurality of clusters based on commonalities between the plurality of replaced strings of data objects of the plurality of data objects; classifying a subset of the data objects in each cluster into at least one class; and aggregating classes within at least one cluster of the plurality of clusters, wherein aggregating classes within each of the at least one cluster includes applying the at least one class for the subset of the data objects in each cluster to each other data object within the cluster.

Abstract: A method and a system for contextually managing and executing a change in security behavior of a target user are provided. The system extracts multiple context attributes including activity telemetry, skill, etc., from multiple external applications. The system dynamically generates one or more security behavioral models for each user based on behavior modeling criteria. The system dynamically generates a security behavior score for each user by scoring a selection of the context attributes from their security behavioral models. The system dynamically generates targeted, contextual control elements specific to a target user identified from among the users using the security behavioral models, the security behavior score, and one or more context libraries. The system dynamically renders one or more of the targeted, contextual control elements on a user device of the target user through one or more delivery channels for executing a change in the security behavior of the target user.