Abstract: Systems and methods for redacted statement delivery to third-party institutions are disclosed. A method may include: receiving a request for a statement from a third-party backend; retrieving a list comprising a plurality of available statements; providing the list of the plurality of available statements to the third-party backend; retrieving a selection of one of the plurality of available statements; identifying metadata for data fields in a statement template; generate the selected statement using the statement template and embedding the metadata in data fields of the statement; identifying a redaction reason; identifying redaction metadata associated with the redaction reason; redact data fields in the statement having metadata matching the redaction reason metadata; storing the redacted statement in a temporary store; and returning a path for the temporary store to the third-party backend, wherein the third-party backend may retrieve the redacted statement from the temporary store.

Abstract: An anomaly detection system is disclosed capable of reporting anomalous processes or hosts in a computer network using machine learning models trained using unsupervised training techniques. In embodiments, the system assigns observed processes to a set of process categories based on the file system path of the program executed by the process. The system extracts a feature vector for each process or host from the observation records and applies the machine learning models to the feature vectors to determine an outlier metric each process or host. The processes or hosts with the highest outlier metrics are reported as detected anomalies to be further examined by security analysts. In embodiments, the machine learnings models may be periodically retrained based on new observation records using unsupervised machine learning techniques. Accordingly, the system allows the models to learn from newly observed data without requiring the new data to be manually labeled by humans.

Abstract: A content management system comprising one or more processing devices, a network interface, and a memory system configured to store programmatic instructions configured to cause the one or more processing devices to perform the following operations is described. An electronic document may be generated and rendered, where the content management system may configure the electronic document as a mesh document, with both forward links and backlinks to other electronic resources. The forward links and/or backlinks may be to local electronic resources or remote electronic resources. The mesh document may be transmitted to client device over an encrypted channel, and the client device may render the electronic document. In response to an activation of a forward or backlink, the corresponding resource may be accessed from a data store, transmitted via the encrypted channel to the client device, and the client device may render such resource.

Abstract: Systems, computer program products, and methods are described herein for implementing device manipulation for counteracting facial recognition authentication security malfeasance. The present invention is configured to receive a request from a user via a client device to access an authentication subsystem to execute a facial recognition authentication for access to an application; initiate the facial recognition authentication on the client device in response to receiving the request; receive, from the client device, one or more external inputs in response to one or more authentication prompts; determine, using a machine learning subsystem, that at least one of the one or more external inputs is associated with a first preconfigured duress code; trigger, using a breach detection subsystem, a first set of security protocols; and execute, using the breach detection subsystem, a first set of predefined actions associated with the first set of security protocols.

Abstract: The present subject matter provides various technical solutions to technical problems facing ADS-B cyber-attacks. One technical solution for detecting and mitigating ADS-B cyber-attacks includes receiving extracting information from received ADS-B signals, detecting a cyber-attack based on a selected subset of ADS-B information, determining a detection probability, and outputting a ADS-B cyber-attack type and probability. This solution may further include determining and implementing a cyber-attack mitigation to reduce the probability or effect of the detected cyber-attack. These solutions operate based on current ADS-B receiver technology, and can be combined with existing ADS-B receivers to detect message injection attacks, modification attacks, and jamming attacks. The technical solutions described herein use machine learning (ML) algorithms and statistical models to detect anomalies in incoming ADS-B messages.

Abstract: A processor with an elliptic curve cryptographic algorithm and a data processing method thereof are shown. The processor has a first register, storing a private key pointer pointing to a private key. In response to a single elliptic curve cryptographic instruction of an instruction set architecture, the processor reads a ciphertext input from a first storage space within a system memory, performing a decryption procedure using the elliptic curve cryptographic algorithm on the ciphertext input based on the private key obtained by referring to the first register to decrypt the ciphertext input and generate a plaintext output, and programming the plaintext output into a second storage space within the system memory.

Abstract: An apparatus method and computer media for implementing a white-box block cipher in a software application to create a secure software application having the same functionality as the software application.

Abstract: Technology is shown for verifying a leaf certificate in a PM chain of trust involving receiving a leaf certificate signed by an intermediate certificate embedded in the leaf certificate. The intermediate certificate is extracted from the received leaf certificate and its public key used to calculate a signature for the received leaf certificate. The calculated signature is compared to a signature included in the received leaf certificate. The received leaf certificate is verified when the calculated signature matches the signature included in the received leaf certificate. The intermediate certificate can be included as a X.509 property of the leaf certificate.

Abstract: This application provides a voiceprint recognition method and device. The method includes: calculating, by an electronic device a first confidence value that an entered voice belongs to a first registered user, and calculating a second confidence value that the entered voice belongs to a second registered user. The method further includes: calculating, by another electronic device, a third confidence value that the entered voice belongs to the first registered user, and calculating a fourth confidence value that the entered voice belongs to the second registered user. A server determines, based on the first confidence value and the third confidence value, a fifth confidence value that a user is the first registered user, and determines, based on the second confidence value and the fourth confidence value, a sixth confidence value that the user is the second registered user.

Abstract: A method comprises: monitoring a data stream comprising a plurality of data events; identifying a data pattern comprising one or more of the plurality of data events; determining that at least one of the data events comprising the data pattern supports virtual card generation; determining that the at least one of the data events comprising the data pattern is performed using a physical card number at a geolocation; determining that at least one virtual number has been associated with profile data associated with a user; transmitting a notification comprising a request to generate a virtual number; and upon receipt of an approval of the request, executing a script to generate the virtual number and associate the virtual number with the geolocation.

Abstract: Techniques are described herein that are capable of provisioning a trusted execution environment (TEE) based on (e.g., based at least in part on) a chain of trust that includes a platform on which the TEE executes. Any suitable number of TEEs may be provisioned. For instance, a chain of trust may be established from each TEE to the platform on which an operating system that launched the TEE runs. Any two or more TEEs may be launched by operating system(s) running on the same platform or by different operating systems running on respective platforms. Once the chain of trust is established for a TEE, the TEE can be provisioned with information, including but not limited to policies, secret keys, secret data, and/or secret code. Accordingly, the TEE can be customized with the information without other parties, such as a cloud provider, being able to know or manipulate the information.

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine-readable instructions that when executed by the processor, may cause the processor to determine that an entity was granted an anomalous role assignment to a managed environment. The processor may also, based on the determination that the role assignment of the entity is anomalous, identify at least one indicator associated with the role assignment, determine an indicator value corresponding to the identified at least one indicator, and determine whether the indicator value exceeds a predefined threshold value. The processor may, based on a determination that the indicator value exceeds the predefined threshold value, output an alert indicating that the role assignment is suspicious.

Abstract: A content distribution system includes content receivers that provide a plurality of blockchain databases that store transaction records associated with subscriber requests for content, and a computer system that processes those transaction records and enables authorized content receivers to output requested content.

Abstract: A dynamic threat landscape to which computer resources of a specific enterprise are subject is tracked. Data feeds maintained by a security system of the enterprise are assessed. The effectiveness of data feed utilization by the security system is quantified, relative to the threat landscape. Threat detection rules deployed by the security system are assessed, and the effectiveness thereof by the security system is quantified. Processing capability of alerts generated by threat detection rules and threat response capability may also be assessed and quantified. The effectiveness of the security system as a whole is automatically quantified, based on the tracked threat landscape, the quantifications of the effectiveness of data feed utilization, threat detection rule utilization, processing capability of alerts generated by threat detection rules and/or threat response capability. Recommendations concerning more effectively protecting the enterprise against specific threats are output.

Abstract: Aspects of the disclosure relate to dynamically controlling access to linked content in electronic communications. A computing platform may receive, from a user computing device, a request for a uniform resource locator associated with an email message. Subsequently, the computing platform may identify that the uniform resource locator associated with the email message corresponds to a potentially-malicious site. In response to identifying that the uniform resource locator associated with the email message corresponds to the potentially-malicious site, the computing platform may determine a risk profile associated with the request received from the user computing device. Based on the risk profile associated with the request, the computing platform may execute an isolation method to provide limited access to the uniform resource locator associated with the email message.

Abstract: In some aspects, the disclosure is directed to methods and systems for providing an architecture for building high performance silicon components that support a rich set of networking and security features. In many implementations, the architecture splits network and security functions into two functional and logical blocks (which may physically be on the same die or integrated circuit in some implementations, or may be split on separate integrated circuits). The network functions may be executed via an integrated network interface card and accelerator subsystem with a high throughput execution pipeline. Security functions may be executed asynchronously from the network processing functions, in many implementations.

Abstract: A node for a VSAN includes a BMC, a processor, and a plurality of VSAN objects. The processor instantiates a Cluster Membership, Monitoring, and Directory Service (CMMDS) and a BMC Service Module (SM). The CMMDS implements a Security Policy and Data Model (SPDM) architecture. The CMMDS determines an inventory list of the VSAN objects and a SPDM authentication state for each of the objects, and provides the inventory list and the SPDM authentication states to the BMC SM. The BMC SM provides the inventory list and the SPDM authentication state to the BMC. The BMC determines that a first VSAN object is not authenticated based upon the SPDM authentication state of the first VSAN object, and directs the CMMDS to halt input/output (I/O) operations on the VSAN to the first VSAN object.

Abstract: Systems, computer program products, and methods are described herein for self-clustering computing protocols. The present invention is configured to detect, using a node analysis engine, a change in a network security protocol associated with a first node or device within a distributed network, and transmit instructions for the first node or device to broadcast the change to nearby nodes or devices such that they can act in concert to protect against identifies security issues.

Abstract: An anomaly detection system is disclosed capable of reporting anomalous processes or hosts in a computer network using machine learning models trained using unsupervised training techniques. In embodiments, the system assigns observed processes to a set of process categories based on the file system path of the program executed by the process. The system extracts a feature vector for each process or host from the observation records and applies the machine learning models to the feature vectors to determine an outlier metric each process or host. The processes or hosts with the highest outlier metrics are reported as detected anomalies to be further examined by security analysts. In embodiments, the machine learnings models may be periodically retrained based on new observation records using unsupervised machine learning techniques. Accordingly, the system allows the models to learn from newly observed data without requiring the new data to be manually labeled by humans.

Abstract: An authentication device includes an image acquisition unit, an identification unit, and an authentication unit. The image acquisition unit acquires an image of an eye of a subject. The identification unit identifies the colored pattern of a colored contact lens worn by the subject by comparing a reference image with the image of the eye. The authentication unit identifies the subject, using a feature in a region other than a colored region of the colored pattern in the iris region of the eye.