Amazon Technologies, Inc.

Donald Lee Bailey, Jr

Nachiketh Rao Potlapally

Eric Jason Brandwine

Andrew Paul Mikulski

Hassan Sultan

Gregory Branchek Roth

Michael David Marr

Robert Eric Fitzgerald

John Schweitzer

James Leon Irving, Jr

Richard Weatherly

John Paul Torres

Abigail Fuller

Giulian Dalton Luz

Gregory Alan Rubin

William Frederick Hingle Kruse

Stephen Edward Schmidt

Jonathan Matthew Miller

Patrick James Ward

Nicholas Howard Brown

Justin Canfield Crites

A namespace monitoring service may track released namespaces such as internet protocol (IP) addresses and manage namespace cooldown pools, available namespace pools, and a registry of released namespaces to detect and mitigate security vulnerabilities that arise from reassignment of namespaces. The namespace monitoring service provides access to the released namespace registry and/or sends a data stream of namespace registry updates. The namespace monitoring service may manage namespace reassignment process and extend the cooldown period of released namespaces or place a hold on available namespaces.

Network namespace monitoring system

A namespace monitoring service may track released namespaces such as internet protocol (IP) addresses and manage namespace cooldown pools, available namespace pools, and a registry of released namespaces to detect and mitigate security vulnerabilities that arise from reassignment of namespaces. The namespace monitoring service provides access to the released namespace registry and/or sends a data stream of namespace registry updates. The namespace monitoring service may manage namespace reassignment process and extend the cooldown period of released namespaces or place a hold on available name spaces.

Methods and apparatus for a computing infrastructure for configurable-quality random data are disclosed. A storage medium stores program instructions that when executed on a processor designate some servers of a provider network as members of a pool of producers of random data usable by random data consumers. The instructions, when executed, determine a subset of the pool to be used to supply a collection of random data intended for a random data consumer, and one or more sources of random phenomena to be used to generate the collection of random data. The instructions, when executed, initiate a transmission of the collection of random data directed to the random data consumer.

Computing infrastructure for configurable-quality random data

Systems, methods, and interfaces for the management of virtual machine instances and other programmatically controlled networks are provided. The hosted virtual networks are configured in a manner such that a virtual machine manager of the virtual network may monitor activity such as user requests, network traffic, and the status and execution of various virtual machine instances to determine possible security assessments. Aspects of the virtual network may be assessed for vulnerabilities at varying levels of granularity and sophistication when a suspicious event or triggering activity is detected. Illustrative embodiments of the systems and methods may be implemented on a virtual network overlaid on one or more intermediate physical networks that are used as a substrate network.

Managing virtual computing testing

Systems, methods, and interfaces for the management of virtual machine networks and other programmatically controlled networks are provided. Hosted virtual networks are configured in a manner such that a virtual machine manager of the virtual network may monitor activity such as user requests, network traffic, and the status and execution of various virtual machine instances to determine possible security assessments. A security assessment may be performed before, after, or simultaneous to the execution of the activity associated with the security assessment event. The execution of an activity may further be synchronous with the results of the security assessment. The timing of the assessment may correspond to the type of assessment or type of activity that is requested or detected.

Anomalous behavior in a multi-tenant computing environment may be identified by analyzing hardware sensor value data associated with hardware events on a host machine. A privileged virtual machine instance executing on a host machine acquires hardware sensor values and causes the values to be compared to other hardware sensor value data that may be indicative of anomalous behavior; for example, various threshold values, patterns, and/or signatures of hardware counter values generated by analyzing and correlating hardware event counter data. In this manner, potential anomalous behavior on an instance may be determined without having to access customer data or workloads associated with the instance.

Detecting anomalous behavior in an electronic environment using hardware-based information

A trusted computing host is described that provides various security computations and other functions in a distributed multitenant and/or virtualized computing environment. The trusted host computing device can communicate with one or more host computing devices that host virtual machines to provide a number of security-related functions, including but not limited to boot firmware measurement, cryptographic key management, remote attestation, as well as security and forensics management. The trusted computing host maintains an isolated partition for each host computing device in the environment and communicates with peripheral cards on host computing devices in order to provide one or more security functions.

Trusted computing host

A graph of a plurality of resources in a computing environment is generated, with the graph associating a first resource of the plurality with a second resource of the plurality. Based at least in part on measurements obtained at a point in a test computing environment that corresponds to a point in the computing environment, a graph representing the relationship between the first resource and the second resource is generated. A threat model identifying potential risks to the computing environment is created from the graph.

Threat detection and mitigation through run-time introspection and instrumentation

A honeypot resource management service receives a request to provision one or more honeypot resources. In response to the request, the service identifies at least one computing resource service that is to be used to present the one or more honeypot resources. The service generates configuration information that is transmitted to the at least one computing resource service to cause the computing resource service to present the one or more honeypot resources to users in accordance with a set of parameters specified in the configuration information.

Managed honeypot intrusion detection system

A graph of a plurality of resources in a computing environment is generated, with the graph associating a first resource of the plurality with a second resource of the plurality. Based at least in part on measurements obtained at a point in a test computing environment that corresponds to a point in the computing environment, an expected value or expected range of values is determined. An assessment of a security state of the computing environment is generated based at least in part on a comparison between a measurement obtained at the point in the computing environment and the expected value or expected range of values, and responsive to a determination that the assessment indicates a rule violation in the computing environment, a security action is performed.

A computer-implemented method includes restricting access to customer data to certain geographic regions authorized by the customer. The restriction can be managed by associating policy information with the customer data that identifies the geographic regions authorized by the customer. Resources attempting to access the customer data can evaluate the policy information associated with the customer data with respect to the geographic location in which the resource is located to determine whether the resource is permitted to access the customer data. The restriction can also be managed by encrypting the customer data with a cryptographic key that corresponds to the customer and/or the authorized geographic regions.

Growing community of inventors

Penn Laird, VA, United States of America

Donald Lee Bailey, Jr