Abstract: Systems and methods for dynamically mitigating a DDOS attack. In an aspect, the technology relates to a computer-implemented method for dynamically mitigating a distributed-denial-of-service (DDOS) attack. The computer-implemented method may include detecting a DDOS attack directing malicious traffic to a target, identifying one or more source locations of the malicious traffic, and in response to detecting the DDOS attack, activating one or more scrub clusters in the identified one or more source locations of the malicious traffic. The method may further include directing traffic intended for the target to the to the activated one or more scrub clusters, detecting an end of the DDOS attack, and in response to detecting the end of the DDOS attack, deactivating the one or more scrub clusters to release hardware resources.

Abstract: A computer implemented method processes alerts. A computer system creates a representation of an alert received for processing. The computer system determines a similarity of the alert with previously processed alerts using the representation of the alert and representations of the previously processed alerts. A first evaluator in the computer system evaluates an alert level for the alert based on previously processed similar alerts in response to the similarity being above a similarity threshold for similar alerts. A second evaluator in the computer system evaluates, the alert level for the alert using a machine learning model in response to the similarity not being above the similarity threshold.

Abstract: A method for generating enterprise cyber reports through linking IP access control logic with error handler and audits compartmentalized by web application for different user groups with multiple monitoring tools data. Business logic may be defined in access control tables for multiple user groups sharing multiple different application data and programmable access control logic applied to subfolders within the website subfolders based on functional user group role permissions. A common network event field name may be used to map multiple different monitoring tools data into common field alias. The field alias mapping allows multiple networking capture tools to be included within the same cyber report. Joining multiple network events field alias with an IP location allows for groups of different IP zone reports to be created within the enterprise being monitored by different monitoring tools.

Abstract: Embodiments of a cyberattack monitoring system are disclosed to identify successful attacks on a service based on benign activities of the attacker performed after the initial attack attempt. In embodiments, the system identifies the initial attack by matching client actions to known attack patterns. Clients observed with attempted attacks are remembered as suspected attackers. The system will then monitor subsequent actions of suspected attackers for signs that the initial attack attempt was successful. In embodiments, a successful attack is recognized when the system observes one or more subsequent benign actions by the suspected attacker. In embodiments, the presence of follow-on benign actions is used as a filter to filter out unsuccessful attacks and false positives detected by the system. The filtering enables the system to better focus system resources and human attention on a small set of client activities that are likely successful attacks.

Abstract: A semiconductor chip includes an electronic hardware circuitry device that includes a plurality of partitionable hardware resources that each includes a corresponding resource allocation state. The electronic hardware circuitry includes a logic control circuit to control access to the plurality of hardware resources based on the respective resource allocation states of the hardware resources and based on input from one or more authorized agents. The semiconductor chip further includes a processor core to implement a plurality of software applications belonging to a first group or to a second group, each of the plurality of applications configured to access and interact with at least one corresponding hardware resource assigned to the respective application, implement assigning software agents each authorized and configured to cause the electronic hardware circuitry device to assign one or more unassigned hardware resources only to one or more of the software applications belonging to certain groups.

Abstract: Techniques for detecting anomalous behavior of an Internet-of-Things (IoT) device in an IoT network. IoT events of an IoT device are captured and analyzed to identify periodic activities of the IoT device. The periodic activities of the IoT device are tracked over time, and variations in the periodic activities are analyzed to assess potential threats to the IoT network.

Abstract: A system and method for detecting cloud identity misuse in a cloud computing environment is presented. The method includes: deploying a runtime sensor on a workload in a cloud computing environment; continuously receiving data from the runtime sensor; generating an activity baseline based on the continuously received data, wherein the runtime sensor is configured to detect runtime processes on the workload; detecting an event in a cloud log, the event including an identifier of the workload; associating a runtime process detected by the runtime sensor on the workload with the event detected in the cloud log; and determining that the event is an anomalous event based on the generated activity baseline.

Abstract: A method for operating a data communication between functional units for a vehicle, in which a predefined number of data packets transmitted by a sending unit to a receiving unit is collected in a data buffer of the sending unit to generate a data block. In each predefined time step, one data packet is transmitted, in which the data packets are collected over a predefined collection period. A signature for authenticating the data block is then determined, the signature being determined over a predefined determination period lasting for multiple time steps. The signature is then sent in multiple parts from the sending unit to the receiving unit over a predefined transmission period, with one part of the signature being sent per time step. The sum of the collection period, the determination period and the transmission period is less than a predefined system fault tolerance time.

Abstract: A method includes receiving a request for a first domain from a requester with an identification, fetching requester profile using the identification, generating a seed domain based on the request and the requester profile, generating a plurality of SLDs based on the seed domain and the requester profile, generating a plurality of TLDs based on the seed domain and the requester profile, generating a first plurality of candidate domains based on the plurality of SLDs and the plurality of TLDs, generating a second plurality of candidate domains that are currently available by checking the first plurality of domains with a domain registrar, ranking the second plurality of candidate domains to be provided to the requester for a selection, receiving a selected domain, automatically updating the requester profile based on the request and the selection, and fetching the updated profile for generating another seed domain for a subsequent request.

Abstract: Methods, apparatuses, or computer program products provide for generating a service risk analysis score data object. A service risk analysis request associated with an unreleased code object is received. One or more service risk analysis attributes are extracted using a service risk analysis layer based at least in part on the unreleased code object. A service risk analysis score data object is generated using a service risk analysis machine learning model based at least in part on the one or more service risk analysis attributes. The service risk analysis score data object is output.

Abstract: In an embodiment, a computer implemented method is provided. The method may include quantifying a plurality of component level risks for at least a subset of components in the network. The method may further include simulating cascades of the component level risks, with each corresponding component designated as a risk seed of the subset of components, throughout the network. The method may additionally include quantifying the network level risk as a risk status in a resilience spectrum based on the simulated cascades.

Abstract: Information characterizing a security event is received from an agent executing on an endpoint computing device. The received information identifies a plurality of files encrypted as part of a ransomware attack and key material used when encrypting each of the files. Based on the received information, a surveyor package is generated which includes decryptor logic to decrypt at least a portion of the files. The surveyor package is deployed to the agent so that it can be unpacked and executed to decrypt at least a portion of the files. Once these files are decrypted, then can be transported to a safe computing environment Related apparatus, systems, techniques and articles are also described.

Abstract: An electronic control unit (ECU), or node, is configured to use a single key for generating requests from a security peripheral for a MAC. The security peripheral includes the stored shared key. The security peripheral may further include a policy enabling it to detect if a request from the V-ECU is valid, in which case it generates a MAC. The security peripheral is also used to store information in a MAC Generate Allow List (MGAL). In some embodiments, the receiving nodes in a network receive data based on a security peripheral's response to a transmit nodes requests for a MAC. The receiving nodes use this knowledge to avoid being spoofed.

Abstract: A method for identifying a malicious connection between a client device and a server includes obtaining handshake parameters for the client device and the server responsive to the client device initiating a connection with the server, generating a feature set by extracting features from the handshake parameters, predicting a maliciousness of the connection using a machine learning model, where the extracted features are provided as inputs to the machine learning model, and automatically initiating a corrective action if the connection is predicted to be malicious.

Abstract: A system and method for performing inspection of a reachable code object of a cloud computing environment is presented. The method includes detecting a network path for each resource of a plurality of resources deployed in a cloud computing environment, wherein the network path includes at least a portion between an external network and the cloud computing environment; determining reachability parameters of each resource of the plurality of resources for which a network path is detected; accessing a code repository including a plurality of code objects; actively inspecting the network path of a resource to determine if the network path is a viable network path; mapping each resource having a viable network path to a code object of the plurality of code objects; inspecting a mapped code object for a cybersecurity object; and initiating a remediation action based on the cybersecurity object.

Abstract: A domain processing system receives or collects raw data containing sample domains each having a known class identity indicating whether a domain is conducting an email campaign. The domain processing system extracts features from each of the sample domains and selects features of interest from the features, including at least a feature particular to a seed domain and features particular to email activities over a time line that includes days before and after a domain creation date. The features of interest are used to create feature vectors which, in turn, are used to train a machine learning model, the training including optimizing a neural network structure iteratively until stopping criteria are satisfied. The trained model functions as an email campaign domain classifier operable to classify candidate domains with unknown class identities such that each of the candidate domain is classified as conducting or not conducting an email campaign.

Abstract: A system includes a plurality of computing nodes that form a blockchain network, wherein one or more of the computing nodes is a metaverse computing node configured to generate a mixed reality environment. A processor of at least one computing node is configured to receive information relating to a suspicious data interaction associated with a data file of a user, simulate, based on the received information, the suspicious data interaction in a synthetic mixed reality environment that is substantially identical to the mixed reality environment, verify the suspicious data interaction while the simulated data interaction is being performed, when the suspicious data interaction cannot be verified, disable one or more future data interactions processed using the same smart contract used to process the suspicious data interaction, and when the suspicious data interaction is successfully verified, terminate the simulated data interaction and process the suspicious data interaction.

Abstract: A system and method for enhancement of device security using machine learning and a set of rules is provided. The system acquires log data from an electronic device configured to communicate data via a network. The system prepares a feature set based on the log data. The feature set corresponds to one or more parameters associated with a cybersecurity of the electronic device. The system determines security incidents associated with the electronic device based on at least one of an application of one or more incident detection rules and/or one or more ML models on the feature set. The system collects information associated with the determined security incidents and determines a set of measures to be implemented on the electronic device to mitigate or prevent issues associated with the security incidents. Thereafter, the system controls execution of the determined set of measures on the electronic device.

Abstract: A device receives a packet from a local network. The packet may be directed toward a cloud computing resource. The device determines that the packet is associated with a new packet flow. In response to determining that the packet is associated with the new packet flow, the device provides one or more packets from the new packet flow to a machine learning model for packet inspection. The device receives an output from the machine learning model and routes the new packet flow based on the output received from the machine learning model. The output indicates whether or not the new packet flow is associated with a network attack.

Abstract: Described herein are systems and methods for verifying the integrity of data, such as data used for training machine learning models. Some implementations are directed to verifying the provenance of datasets, the contents of datasets, or both. In some implementations, multiple filters are selected for verifying the contents of datasets. Filters can be selected based on rules, random selection, or using a machine learning model in some implementations. In some implementations, data cleaning is provided.

Abstract: The present disclosure relates to a system, method, and computer program for adjusting operations of a customer service application based on metrics generated as a result of substantially real-time monitoring of entity states within the customer service application. The system receives entity events from a plurality of services associated with the customer service application. It tracks states of entities in substantially real time within the customer service application based on the events and the state machine logic for the customer service application, including identifying any entities in an anomalous state. For each non-anomalous state transition, one or more state transition measurements are calculated. The system generates metrics for the customer service application based on the state transition measurements for entities in a non-anomalous state and adjusts the operations of the customer service application in substantially real time based on the metrics.

Abstract: A computer implemented method of feature detection in temporal graph data structures of events, the method including receiving a temporal series of graph data structures of events each including a plurality of nodes corresponding to events and edges connecting nodes corresponding to relationships between events; rendering each graph data structure in the series as an image representation of the graph data structure including a representation of nodes and edges in the graph being rendered reproducibly in a cartesian space based on attributes of the nodes and edges, so as to generate a temporal series of image representations ordered according to the temporal graph data structures; processing the series of image representations by a convolutional neural network to classify the image series so as to identify a feature in the image series, the convolutional neural network being trained by a supervised training method including a plurality of training example image series in which a subset of the training examples

Abstract: A computer system comprising a processor and a memory storing instructions that, when executed by the processor, cause the computer system to perform a set of operations. The set of operations comprises collecting domain attribute data comprising one or more domain attribute features for a domain, collecting sampled domain profile data comprising one or more domain profile features for the domain and generating, using the domain attribute data and the sampled domain profile data, a domain reputation assignment utilizing a neural network.

Abstract: A system and method to identify and prevent cybersecurity attacks on modern, highly-interconnected networks, to identify attacks before data loss occurs, using a combination of human level, device level, system level, and organizational level monitoring.

Abstract: Malware signature generation through combination rule mining is disclosed. A set of properties associated, collectively, with a plurality of data samples is received. A first data sample has a first set of properties and a second data sample has a second set of properties. A combination signature comprising at least a first property included in the first set of properties and a second property included in the second set of properties is generated.

Abstract: Systems and methods of discovering computer network assets, including: identifying, by a processor, in sampled traffic over at least one computer network, an internet protocol (IP) address of a node communicating over at least one port, wherein the at least one port is associated with an asset type, determining, by the processor, a volume of traffic associated with the IP address of the node communicating over the at least one port, discovering, by the processor, the IP address of the node as belonging to an asset of the asset type, based on the volume of traffic exceeding a dynamic threshold, and adding the asset, by the processor, to a list of discovered assets.

Abstract: A system for detecting Denial-of-Service (DoS) attacks on one or more user profiles collects a number of invalid sign-on attempts on the one or more user profiles during every time interval. The system determines a number of invalid sign-on attempts on every user profile since the start of the first time interval. The system detects a first DoS attack on a particular user profile if a first number of invalid sign-on attempts on the particular user profile exceeds a single-user profile. The system detects a second DoS attack on multiple user profiles during the first time interval if the increase in the total number of invalid sign-on attempts since the last time interval exceeds a scan-level threshold number. The system detects a third DoS attack on multiple user profiles if the total number of invalid sign-on attempts detected during combined time intervals exceeds a third threshold number.

Abstract: This specification generally relates to methods and systems for applying network policies to devices based on their current access network. One example method includes identifying a proxy connection request sent from a particular client device to a proxy server over a network, the proxy connection request including a hostname and configured to direct the proxy server to establish communication with the computer identified by the hostname on behalf of the client device; determining an identity of the client device based on the proxy connection request; identifying a domain name system (DNS) response to a DNS request including the hostname from the proxy connection request; and updating DNS usage information for the particular client based on the identified DNS response including the hostname from the proxy connection request.

Abstract: An image processing system includes: a first processor that is mounted on an image processing apparatus including at least one of an image forming unit that performs processing of forming an image on a medium or an image scanning unit that performs processing of scanning an image on a medium; and a second processor that is mounted on a control apparatus that is connected to the image processing apparatus via a virtual leased line, and controls a part of at least one of the processing of forming an image on a medium or the processing of scanning an image on a medium performed by the image processing apparatus, in which the first processor is configured to: in a case where an event to be recorded in a log occurs in the image processing apparatus, transmit log information regarding the event to the control apparatus via the virtual leased line being connected or after connecting the virtual leased line in a case where the virtual leased line is not connected; and the second processor is configured to: in a case

Abstract: The disclosed embodiments include systems and methods for performing operations using least-privilege access to and control of target network resources. Operations may include identifying a prompt associated with a least-privilege requesting identity to initiate an action on a target network resource; executing, in response to the prompt, a first set of executable code; initiating, based on the first set of executable code, execution of a second set of executable code on the target network resource, wherein the second set of executable code executes using a least-privilege credential or using least-privilege permissions, the least-privilege credential and the least-privilege permissions being determined according to a least-privilege security policy associated with a type of activity expected to be performed on the target network resource; and instructing the second set of executable code to perform the action remotely on the target network resource through a remote session.

Abstract: Disclosed are techniques to detect and prevent malware attacks, and more specifically, a subset of malware attacks called ransomware (which is not to suggest that the disclosed techniques are not applicable to detecting other types of malware attacks that exhibit some of the same behaviors). Example embodiments disclose systems, like a ransomware protection service (RPS) and platform, and techniques employed by such systems to detect and stop ransomware from encrypting files and systems. Disclosed techniques implemented by the RPS and platform are expected to provide protection against new or unknown ransomware malware, also known as zero-day attacks.

Abstract: Methods, apparatuses, and systems related to operations for memory process feedback. A controller can monitor memory activities, such as processes, identify row hammer aggressors, and perform mitigating steps to the row hammer aggressors. The controller may have a table of addresses of row hammer aggressors and perform operations of tracking row hammer aggressors. The controller can determine whether the number of aggressors reaches a threshold. When the number of aggressors reaches the threshold, the controller can send a message with the aggressor addresses to the operating system. The operating system can perform mitigating steps to the row hammer aggressors. In some embodiments, the controller may identify the row hammer aggressors and inject poisoned data into the process to mitigate the row hammer aggressors.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a first group of Internet Protocol (IP) addresses from a group of network devices, and determining a second group of IP addresses from the first group of IP addresses includes possible malicious IP addresses utilizing a machine learning application. Further embodiments can include obtaining a first group of attributes of malicious IP addresses from a first repository, and determining a third group of IP addresses from the second group of IP addresses includes possible malicious IP addresses based on the first group of attributes. Additional embodiments can include receiving user-generated input indicating a fourth group of IP addresses from the third group of IP addresses includes possible malicious IP addresses, and transmitting a notification to a group of communication devices indicating that the fourth group of IP address includes possible malicious IP addresses. Other embodiments are disclosed.

Abstract: Systems and methods related to flush plus reload cache side-channel attack mitigation are described. An example method for mitigating a side-channel timing attack in a system including a processor having at least one cache is described. The method includes receiving a first instruction, where the first instruction, when executed by the processor, is configured to flush at least one cache line from the at least one cache associated with the processor. The method further includes, prior to execution of the first instruction by the processor, automatically mapping the first instruction to a second instruction such that the at least one cache line is not flushed from the at least one cache even in response to receiving the first instruction.

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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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 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.