Abstract: A method and apparatus for path detection are disclosed. In the method and apparatus, a data path may link two path-end nodes in a network. Event data for the network may be received and may be used to determine, for each node resident on the path, proximity measures to each path-end node. The proximity measure of network nodes may be evaluated to determine whether a path exists between the two path-end nodes.

Abstract: Described are techniques for identifying anomalous and non-anomalous requests based on metric values determined from a request. Weights to be associated with particular metric values may be determined based on metric data for those values. The metric data may indicate a total number of accesses by requests having a particular metric value, a frequency of access, or particular access times. Based on the weight values and the metric values for the request, a security score for the request may be determined. The security score may indicate a confidence that the request is anomalous or non-anomalous. Potentially anomalous requests may be determined to be non-anomalous if the metric values correspond to known sets of metric values, determined from previous requests. In some cases, metric data may be normalized prior to use to facilitate faster queries and conserve available data storage.

Abstract: Disclosed are various embodiments for active data that tracks usage. The active data includes instructions that are executable by a computing device. The computing device is scanned to identify characteristics of the computing device. The characteristics of the computing device are utilized to determine whether the usage of the active data is authorized. Data is transmitted to a network service, including identifying information for the particular computing device and data that identifies a deployment of the active data.

Abstract: Techniques are described for employing a crowdsourcing framework to analyze data related to the performance or operations of computing systems, or to analyze other types of data. A question is analyzed to determine data that is relevant to the question. The relevant data may be decontextualized to remove or alter contextual information included in the data, such as sensitive, personal, or business-related data. The question and the decontextualized data may then be presented to workers in a crowdsourcing framework, and the workers may determine an answer to the question based on an analysis or an examination of the decontextualized data. The answers may be combined, correlated, or otherwise processed to determine a processed answer to the question.

Abstract: Disclosed are various embodiments for obtaining policy data specifying decoy data eligible to be inserted within a response to an access of a data store. The decoy data is detected in the response among a plurality of non-decoy data based at least upon the policy data. An action associated with the decoy data is initiated in response to the access of the data store meeting a configurable threshold.

Abstract: A method and apparatus for path detection are disclosed. In the method and apparatus, a data path may link two path-end nodes in a network. Event data for the network may be received and may be used to determine, for each node resident on the path, proximity measures to each path-end node. The proximity measure of network nodes may be evaluated to determine whether a path exists between the two path-end nodes.

Abstract: A method and apparatus for path detection are disclosed. In the method and apparatus, a data path may link two path-end nodes in a network. Event data for the network may be received and may be used to determine, for each node resident on the path, proximity measures to each path-end node. The proximity measure of network nodes may be evaluated to determine whether a path exists between the two path-end nodes.

Abstract: A technology is described for making a decision based on identifying without disclosing the identifying information. The method may include receiving a mapping value that represents identifying information that has been converted into a mapping value. A request for data associated with the identifying information may be made by providing the mapping value as a proxy for the identifying information whereby the data associated with the identifying information may be located using the mapping value and returned to a requesting client or service.

Abstract: Disclosed are various embodiments for active data that tracks usage. The active data includes instructions that are executable by a computing device. The computing device is scanned to identify characteristics of the computing device. The characteristics of the computing device are utilized to determine whether the usage of the active data is authorized. Data is transmitted to a network service, including identifying information for the particular computing device and data that identifies a deployment of the active data.

Abstract: Techniques are described for identifying anomalous execution instance of the process as a security risk by analyzing the memory allocation for the process. Performance data describing memory allocation data describing the amount of memory allocated for a process or utilized by a process during its execution. A baseline of memory allocation is established for the plurality of executions of the process by applying a statistical distribution to the performance data collected. A memory allocation for the executing process may be compared to the baseline. An anomalous execution instance of the process that is outside a predetermined number of statistical variances of the baseline may be determined. At least one anomalous execution instance of the process may be designated as a security risk based at least partly on the anomalous memory allocation.

Abstract: Disclosed are various embodiments for active data, such as active decoy data. The active decoy data includes instructions that, when executed by a particular device, cause the particular computing device to determine whether the particular computing device is a target computing device. The particular computing device initiates a predefined action in response to determining that the particular computing device is not the target computing device. The approaches described herein may also be useful in wrapping and distributing digital content.

Abstract: Methods and systems for framing detection are disclosed. A web page comprising a child frame is generated. The child frame comprises an instruction to load a component from a merchant. The child frame comprises a header option restricting a loading of the component from within a parent frame associated with a domain external to the merchant. The web page is sent from the merchant to a client browser. It is determined that the web page is loaded within the parent frame in the client browser if a request for the component is not received by the merchant. It is determined that the web page is not loaded within the parent frame in the client browser if a request for the component is received by the merchant.

Abstract: Techniques are described for identifying potential code injection attacks against a process by analyzing the memory allocation for the process. Memory allocation data may be collected on one or more host computing devices, the memory allocation data describing the amount of memory allocated for a process or utilized by a process during its execution. The collected memory allocation data may be analyzed to identify instances of anomalous memory allocation during process execution. Statistical or machine learning algorithms may be employed to identify anomalous memory allocation based on the analysis of aggregated memory allocation data for the process.

Abstract: Techniques are described for employing a crowdsourcing framework to analyze data related to the performance or operations of computing systems, or to analyze other types of data. A question is analyzed to determine data that is relevant to the question. The relevant data may be decontextualized to remove or alter contextual information included in the data, such as sensitive, personal, or business-related data. The question and the decontextualized data may then be presented to workers in a crowdsourcing framework, and the workers may determine an answer to the question based on an analysis or an examination of the decontextualized data. The answers may be combined, correlated, or otherwise processed to determine a processed answer to the question. Machine learning techniques are employed to adjust and refine the decontextualization.

Abstract: Disclosed are various embodiments for obtaining policy data specifying decoy data eligible to be inserted within a response to an access of a data store. The decoy data is detected in the response among a plurality of non-decoy data based at least upon the policy data. An action associated with the decoy data is initiated in response to the access of the data store meeting a configurable threshold.

Abstract: Techniques are described for graph-based analysis of event data in a computing environment. Event data is collected from host devices, the event data describing events in which devices, processes, or services are accessed in the environment. The event data is arranged into a graph that includes vertices corresponding to devices, processes, or services, and edges that connect pairs of vertices. Each edge may identify an event by connecting two vertices corresponding to two devices, processes, or services included in the event. A rarity metric is determined for each edge, indicating a rarity of events of a particular type connecting two vertices. A risk metric may also be determined for each edge, indicating a security risk associated with the event type or the target of the event. The graph may be traversed according to the risk and rarity metrics, to identify patterns of anomalous activity in the event data.

Abstract: Information security may include defending information from unauthorized access, use, disclosure, modification, destruction, and so forth. Described herein are systems, methods and devices for enabling a user device to implement functions for dynamically identifying and reacting to potentially malicious activity. In one example, a user device configures a sentinel node to identify potentially malicious behavior by causing the sentinel node to analyze data from selected emitter nodes and selected algorithms. The user device may also specify how the sentinel node reacts to potential malicious activity.

Abstract: Disclosed are various embodiments for obtaining policy data specifying decoy data eligible to be inserted within a response to an access of a data store. The decoy data is detected in the response among a plurality of non-decoy data based at least upon the policy data. An action associated with the decoy data is initiated in response to the access of the data store meeting a configurable threshold.

Abstract: A technology is described for making a decision based on identifying without disclosing the identifying information. The method may include receiving a mapping value that represents identifying information that has been converted into a mapping value. A request for data associated with the identifying information may be made by providing the mapping value as a proxy for the identifying information whereby the data associated with the identifying information may be located using the mapping value and returned to a requesting client or service.