Abstract: Systems and methods are described for monitoring third party services. A system may receive a set of input signals from third-party sources. The system may characterize rules and models for detecting organizational attributes based on the input signals to identify computer threats, attacks, performance bottlenecks, or availability issues, and other indicators of potential risk for an organization. The system may characterize the rules and models based on gaps in coverage, system updates, and consistency across multiple entities.

Abstract: Systems and methods are described for identifying computer risks. A system may receive a set of input signals from third-party sources. The system may identify computer risks based on the input signals using a hypothesis generation component. The hypothesis generation component may be designed to identify computer risk based on a current state of a network environment, changes to a risk profile, and critical attack path predictions. The hypothesis generation component may also be designed to evaluate the identified computer risks to prioritize the computer risks, identify a threat hunting hypothesis for each type of risk, and identify migration techniques for the prioritized risks.

Abstract: Systems and methods are described for identifying computer risk. A system may receive a set of input signals from third party sources. The system may normalize the input signals for uniformity. The system may then classify the normalized input signals. The system may then vectorize the classified input signals via a machine learning model. After vectorization, the system may cluster alerts from within the vectorized input signals regarding computer risk.

Abstract: Systems and methods are described for identifying computer risk. A system may receive a set of input signals from independent sources. The system may use a header tracing component to discover and manage service dependencies and maps based on the input signals. The system may then configure new content sources or modify existing content sources based on service dependencies and maps. After configuration, the system may detect configuration, and setup monitoring of alert rules.

Abstract: Systems and methods are described for identifying computer risk. A system may receive a set of input signals from third party sources. The system may characterize the input signals to derive an attack graph. The system may then emulate a threat or attack based on the attack graph. After emulation, the system may determine a risk score based on the attack graph.

Abstract: A system that includes one or more priority failure detectors may be included that detect node or process failures in the distributed computer network. The system has a fault-tolerant, client-server architecture where a client process presents a particular consensus problem to one or more server processes to solve such a consensus based problem. The system assigns priority levels to processes involved in a consensus session and controls the frequencies of their heartbeat status messages based on their respective priority levels. By controlling the frequencies, the reliability of the network is enhanced and the overall message load on the even bus is reduced to a minimum number. The system also discloses a name service that assigns unique logical identities to all processes in a consensus session. Further, by tagging all involved processes appropriately, multiple consensus based problems can be dealt with on a set of consensus server processes simultaneously.