Methods and systems for assessing internet exposure of a cloud-based workload are disclosed. A method comprises accessing at least one cloud provider API to determine a plurality of entities capable of routing traffic in a virtual cloud environment associated with a target account containing the workload, querying the at least one cloud provider API to determine at least one networking configuration of the entities, building a graph connecting the plurality of entities based on the networking configuration, accessing a data structure identifying services publicly accessible via the Internet and capable of serving as an internet proxy; integrating the identified services into the graph; traversing the graph to identify at least one source originating via the Internet and reaching the workload, and outputting a risk notification associated with the workload. Systems and computer-readable media implementing the above method are also disclosed.

Embodiments of the present disclosure provide systems, methods, and non-transitory computer storage media for detecting abnormal behavior of device in an enterprise network based on an analysis of behavioral information of the device's neighbors in network. At a high level, embodiments of the present disclosure employ a hive-mind approach to determine anomalous behavior of a device in a network based on analyzing behavior information reported by neighboring devices within the network. Embodiments identify that a device is alive and connected within the network based on multiple neighboring devices reporting behavioral information about the device; however, the device may be dysfunctional and failing to report its own information. By aggregating and analyzing behavioral information of a device based on the reporting information of its neighboring devices, embodiments of the present disclosure are able to determine whether a device is healthy even when the device is unable to report its own information.

An artefact is received. Features are later extracted from the artefact and are used to populate a vector. The vector is input into a classification model to generate a score. This score is then modified using a time-based oscillation function and is provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

A computer-implemented method includes: receiving system information data representing configurations of digital systems; receiving attack information data associated one or more of the digital systems; analyzing the received system information data and attack information data, to associated attack types; identifying, for each identified attack type, correlations and/or causalities between individual system constituents or combinations thereof in the digital systems associated with attacks; determining and assigning, based on the identified correlations and/or causalities, an attack vulnerability value, for each attack, respectively, to each of the systems and/or systems' constituents and/or combinations thereof; and retrievably storing attack vulnerability values associated with the systems, system constituents and/or combinations thereof.

Techniques and systems to provide a more intuitive user overview of events data by mapping unbounded incident scores to a fixed range and aggregating incident scores by different schemes. The system may detect possible malicious incidents associated with events processing on a host device. The events data may be gathered from events detected on the host device. The incident scores for incidents may be determined from the events data. The incident scores may be mapped to bins of a fixed range to highlight the significance of the incident scores. For instance, a first score mapped to a first bin may be insignificant while a second score mapped to a last bin may require urgent review. The incident scores may also be aggregated at different levels (e.g., host device, organization, industry, global, etc.) and at different time intervals to provide insights to the data.

A computer-implemented method includes monitoring, by a power monitor on a computer device, for a peripheral device connection. The peripheral device connection connecting a peripheral device to an input/output port of the computer device. The input/output port is configured to provide power from a power supply of the computer device to the peripheral device. In response to the monitoring for the peripheral device connection identifying the peripheral device connection, the method includes determining, by the power monitor, a device type and a negotiated power of the peripheral device as connected. The power monitor determines whether the negotiated power of the peripheral device as connected matches expected power information. In response to determining the negotiated power of the peripheral device does not match the expected power information, the power monitor takes action on the computer device.

In an example, a method includes providing a computing device with an instruction to cause the computing device to execute the instruction. The method further includes monitoring a side channel of a microarchitectural component of the computing device to obtain an indication of whether or not a state of the microarchitectural component changes as a result of the computing device executing the instruction. The method further includes determining whether or not the indication corresponds to an expected state of the microarchitectural component for the instruction.

Security can be improved in a business application or system, such as a mission-critical application, by automatically analyzing and detecting anomalies for mission-critical applications. This detection may be based on a dynamic analysis of business process logs and audit trails that includes User and Entity Behavior Analysis (“UEBA”).

Methods and systems for allowing software components that operate at a specific exception level (e.g., EL-3 to EL-1, etc.) to repeatedly or continuously observe or evaluate the integrity of software components operating at a lower exception level (e.g., EL-2 to EL-0) to ensure that the software components have not been corrupted or compromised (e.g., subjected to malware, cyberattacks, etc.) include a computing device that identifies, by a component operating at a higher exception level (“HEL component”), at least one of a current vector base address (VBA), an exception raising instruction (ERI) address, or a control and system register value associated with a component operating at a lower exception level (“LEL component”). The computing device may perform a responsive action in response to determining that the current VBA, the ERT address, or control and system register value do not match the corresponding reference data.

Systems and methods are disclosed for event-based application control. A system extension is configured to leverage an endpoint security API for monitoring event activity within operating system kernel processes. The system extension registers with the endpoint security API particular event types for which the system extension would like to receive notifications. In response to receiving notifications regarding detected events corresponding to the registered event types, the system extension determines if the event, and its corresponding process, are safe and allowable to execute. In various embodiments, the system leverages whitelists, blacklists, and rules policies for making a safeness determination regarding the event notification. The system extension transmits this determination to the operating system via the endpoint security API.