Abstract: A mechanism for building decentralized computer applications that execute on a distributed computing system. The present technology works within a web browser, client application, or other software and provides access to decentralized computer applications through the browser. The present technology is non-custodial, wherein a public-private key pair, which represents user identity, is created on a client machine and then directly encrypted by a third-party platform without relying on one centralized computing system.

Abstract: Conditionally initiating a security measure in response to an estimated increase in risk imposed related to a particular user of a computing network. The risk is determined using a rolling time window. Accordingly, sudden increases in risk are quickly detected, allowing security measures to be taken quickly within that computing network. Thus, improper infiltration into a computing network is less likely to escalate or move laterally to other users or resources within the computing network. Furthermore, the security measure may be automatically initiated using settings pre-configured by the entity. Thus, the security measures go no further than what the entity instructed, thereby minimizing risk of overreaching with the security measure.

Abstract: Methods, systems, and computer readable media for network security are described. In some implementations, security tasks and roles can be allocated between an endpoint device and a firewall device based on tag information sent from the endpoint, the tag information including one or more characteristics of a traffic flow, information of resource availability, and/or reputation of a process associated with a traffic flow.

Abstract: The present disclosure relates to a wireless token capable of representing a user network, the token being used to automatically provision an IoT enabled device to connect to the user network. Functions required to achieve this include: authenticate the token with the user network, and responsive to said authentication, obtain and store configuration information for enabling the token to communicatively couple one or more devices at or within a defined proximity to the token, with the user network; responsive to a wireless signal received from a given device among the one or more devices, establish a temporary secure communication channel between the given device and the token; and provide the configuration information from the token to the given device using the temporary secure communication channel, wherein the configuration information enables the given device to establish a connection with and operate in the user network based on the obtained configuration information.

Abstract: Disclosed are techniques for performing forensic analysis of computer systems in a cloud network. The techniques can include using a scalable, cloud-based, specialized computer architecture for performing the forensic analysis of computer systems.

Abstract: Disclosed herein are methods, systems, and processes for probabilistically identifying anomalous levels of honeypot activity. A honeypot dataset associated with a honeypot network is received and a representative usage value is determined from the honeypot dataset. The representative usage value is identified as being associated with anomalous behavior if the representative usage value deviates from an expected probability distribution. A remediation operation is initiated in the honeypot network in response to the identification of the representative usage value as being associated with the anomalous behavior by virtue of the representative usage value deviating from the expected probability distribution.

Abstract: A system generates network perimeter for an organization based on the connection data. The system builds a model, for example, a machine learning based model configured to receive a network zone as input and output a score indicating security of the network zone. The system receives information describing connection requests received from client devices associated with the organization. The system adjusts parameters of the machine learning based model based on information describing the connection requests. The adjusting of the machine learning based model improves the accuracy of prediction based on the information describing the connection requests. The system determines a network perimeter for the organization using the machine learning based model. The network perimeter may be used for implementing a network policy for the organization based on the determined network perimeter.

Abstract: Artificial Intelligence (“AI”) apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.

Abstract: Implementations include receiving an AAG that at least partially defines a digital twin of an enterprise network and includes rule nodes each representing an attack tactic that can be used to move along a path, determining security controls each mitigating at least one rule node, executing an iteration of a simulation of a sub-set of security controls in the enterprise network, the iteration including: for each security control in the set of security controls, determining, an influence score that represents a change in a security risk from implementing the security control and a rule distribution, defining the sub-set of security controls based on the first influence scores, and reducing the AAG based on the sub-set of security controls to provide a residual AAG, determining a decrease in a graph risk value and the first AAG, and selectively implementing the sub-set of security controls in the enterprise network.

Abstract: According to an embodiment, a communication control device includes a communication unit and a jamming control unit. The communication unit wirelessly communicates with a first device and uploads information acquired from the first device to a second device via a network. The jamming control unit allows jamming radio waves to be output to interfere with wireless communication between the first device and a device other than the communication control device, the device being to perform wireless communication in a communication band used by the first device.

Abstract: A threat management system provides a collection of queries for investigating security issues within an enterprise. Useful inferences are drawn about the value of different queries, and about the security posture of the enterprise, by monitoring contextual activity such as the popularity and context of query usage, patterns of end user modification to queries, and post-query activity.

Abstract: Briefly, systems and methods for managing Internet of Things (IoT) devices provide platforms featuring an architecture for user and device authentication as well as IoT system self-healing.

Abstract: A method, in particular a computer-implemented method, for processing data of a technical system. The method includes the following steps: ascertaining first pieces of information which are associated with a data traffic of the system, and ascertaining metadata associated with the data traffic of the system based on the first pieces of information.

Abstract: Protecting data in non-volatile storages provided to clouds against malicious attacks. According to an aspect, multiple malicious patterns indicating respective malicious attacks to access non-volatile storages provided to clouds in a cloud infrastructure are maintained. When an access request is received, the data stream representing the access request is examined to determine whether the data stream contains any of the malicious patterns. If the data stream is found not to contain any malicious pattern, it is concluded that the access request is free of the malicious attacks. If the data stream is found to contain at least one malicious pattern, it is concluded that the access request is a malicious attack corresponding to the malicious pattern.

Abstract: A security module for a CAN node includes a RXD input interface for receiving data from a CAN bus, TXD output interface for transmitting data to the CAN bus, and a RXD output interface for providing data to a local controller. The security module is configured to receive a CAN frame from the CAN bus. The CAN frame includes a CAN message. The security module is also configured to compare an identifier of the received CAN frame with at least one identifier associated with the local controller; and upon detection of a match between the identifier of the received CAN frame and the at least one identifier associated with the local controller: pass the CAN message to the local controller via the RXD output interface; decouple the local controller from the CAN bus; and invalidate the CAN message on the CAN bus via the TXD output interface.

Abstract: Methods and systems for artificially intelligent security incident and event management using an attention-based deep neural network and transfer learning are disclosed. A method includes: collecting, by a computing device, system and network activity events in bulk; forming, by the computing device, a corpus using the collected system and network activity events; correlating, by the computing device, discrete events of the system and network activity events into offenses; adding, by the computing device, additional features to the corpus representing the offenses and disposition decisions regarding the offenses; training, by the computing device, a deep neural network using the corpus; and tuning, by the computing device, the deep neural network for a monitored computing environment using transfer learning.

Abstract: Systems, methods, and apparatuses for anomalous user behavior detection and risk-scoring individuals are described. User activity data associated with a first computing device of a first user is received from an agentless monitoring data source different from the first computing device. The user activity data includes a user identifier. An active directory (AD) identifier and employee-related information from a human resources database are determined based on the user identifier. Based on the employee-related information and/or AD identifier, a probability of an adverse event is determined. When the probability of the adverse event exceeds a predetermined threshold, a logging agent is activated on the first computing device and additional user activity data is received from the logging agent.

Abstract: A method non-transitory computer readable medium, device and system that receives one of one or more requests from a client to a web server system. An interstitial page is served to the client and comprises instrumentation code that, when executed at the client, collects telemetry data. The telemetry data is received and a threat analysis is performed on the telemetry data collected in association with the one of the requests. A determination is made on when, based on the performing the threat analysis, that the one of the requests is from a potential attacker. When the determination indicates the one of the requests is not from the potential attacker then the one of the requests is allowed.

Abstract: Techniques are provided for detecting an intrusion event in a network. At a gateway device in the network, performance parameters of the gateway device are monitored. Steady-state operations are defined based on expected performance of the device parameters. The steady-state operations are compared to the monitored device performance and other context parameters. A hybrid network intrusion detection technique is activated to determine a presence of the intrusion event for known and unknown attacks.

Abstract: Methods and systems for providing secure digital access to services are described. Embodiments include user behavior tracking, learning, and updating one or more contextual access algorithms and thereafter can act as multi-factor authentications. The method may include receiving data for a group of users and initializing a machine learning algorithm with the group data. The method may also collect individual user data and context data periodically, including characteristic behavior data, and update the machine learning algorithm with the individual user data. The method may further calculate a threshold for tolerance based on the updated algorithm, and verify user requests for access to the service. A multi-factor authentication may be presented to the user when the verifications are not acceptable, such as by being below a threshold. A permissions data structure can be generated and used to control access to the service.