Abstract: Automated malware signature generation is disclosed. Automated malware signature generation includes monitoring incoming unknown files for the presence of malware and analyzing the incoming unknown files based on both a plurality of classifiers of file behavior and a plurality of classifiers of file content. An incoming file is classified as having a particular malware classification based on the analyzing of incoming unknown files and a malware signature is generated for the incoming unknown file based on the particular malware classification. Access is provided to the malware signature.

Abstract: The present disclosure describes methods, systems, and computer program products for providing data warehouse model federation. One computer-implemented method includes receiving a request to transform a first semantically enriched model to a second semantically enriched model, wherein the first semantically enriched model operates in a first data warehouse and the second semantically enriched model operates in a second data warehouse; identifying a set of data associated with the first semantically enriched model, wherein the set of data is stored in the first data warehouse; providing access to the set of data associated with the first semantically enriched model from the second data warehouse; creating the second semantically enriched model based on the first semantically enriched model using an interface of the second data warehouse; and deploying the second semantically enriched model in the second data warehouse.

Abstract: Automated malware signature generation is disclosed. Automated malware signature generation includes monitoring incoming unknown files for the presence of malware and analyzing the incoming unknown files based on both a plurality of classifiers of file behavior and a plurality of classifiers of file content. An incoming file is classified as having a particular malware classification based on the analyzing of incoming unknown files and a malware signature is generated for the incoming unknown file based on the particular malware classification. Access is provided to the malware signature.

Abstract: Automated malware signature generation is disclosed. Automated malware signature generation includes monitoring incoming unknown files for the presence of malware and analyzing the incoming unknown files based on both a plurality of classifiers of file behavior and a plurality of classifiers of file content. An incoming file is classified as having a particular malware classification based on the analyzing of incoming unknown files and a malware signature is generated for the incoming unknown file based on the particular malware classification. Access is provided to the malware signature.

Abstract: The present disclosure describes methods, systems, and computer program products for providing data warehouse model federation. One computer-implemented method includes receiving a request to transform a first semantically enriched model to a second semantically enriched model, wherein the first semantically enriched model operates in a first data warehouse and the second semantically enriched model operates in a second data warehouse; identifying a set of data associated with the first semantically enriched model, wherein the set of data is stored in the first data warehouse; providing access to the set of data associated with the first semantically enriched model from the second data warehouse; creating the second semantically enriched model based on the first semantically enriched model using an interface of the second data warehouse; and deploying the second semantically enriched model in the second data warehouse.

Abstract: In one embodiment, a computer-implemented method comprises receiving a query. A data store stores data as a plurality of data sets. Each data set comprises a plurality of fields and a plurality of data elements. Each field is associated with a portion of data elements. The query identifies selected data sets and selected properties of the selected data sets. For each selected property, the data elements of the selected data sets associated with each selected property are determined. A first rating of the determined data elements of the selected data sets is determined based on a type of combination of a pair of selected data sets. For the selected data set pairs, a second rating of the pair is determined based on the first ratings for the selected properties. A model of a combination of the selected data sets is generated based on the second rating.

Abstract: A central data warehouse includes embedded data marts. These embedded data marts, referred to as workspaces, are assigned centrally manage data by reference only but rely directly on the centrally managed data and the underlying infrastructure. Workspaces still allow departments in an enterprise to perform certain actions on their own (like adding new data and building new models) without having to instantiate copies of the centrally managed data in a locally managed data mart.

Abstract: A system and method for uploading data from a customer system to a hosted system is disclosed. A stub is integrated with a firewall between the customer system and the hosted system. The stub includes an inbound layer on the customer system side of the firewall and an outbound layer on the hosted system side of the firewall, and the inbound layer includes a write-only directory. A demon is connected between the inbound layer and the outbound layer of the stub. The demon is configured to recognize newly received data in the write-only directory of the inbound layer, encrypt the newly received data to generate encrypted data, and move the encrypted data to the outbound layer for access by the hosted system.

Abstract: A central data warehouse includes embedded data marts. These embedded data marts, referred to as workspaces, are assigned centrally manage data by reference only but rely directly on the centrally managed data and the underlying infrastructure. Workspaces still allow departments in an enterprise to perform certain actions on their own (like adding new data and building new models) without having to instantiate copies of the centrally managed data in a locally managed data mart.

Abstract: In one embodiment, a computer-implemented method comprises receiving a query. A data store stores data as a plurality of data sets. Each data set comprises a plurality of fields and a plurality of data elements. Each field is associated with a portion of data elements. The query identifies selected data sets and selected properties of the selected data sets. For each selected property, the data elements of the selected data sets associated with each selected property are determined. A first rating of the determined data elements of the selected data sets is determined based on a type of combination of a pair of selected data sets. For the selected data set pairs, a second rating of the pair is determined based on the first ratings for the selected properties. A model of a combination of the selected data sets is generated based on the second rating.

Abstract: Automated malware signature generation is disclosed. Automated malware signature generation includes monitoring incoming unknown files for the presence of malware and analyzing the incoming unknown files based on both a plurality of classifiers of file behavior and a plurality of classifiers of file content. An incoming file is classified as having a particular malware classification based on the analyzing of incoming unknown files and a malware signature is generated for the incoming unknown file based on the particular malware classification. Access is provided to the malware signature.

Abstract: An assembly (10) for installation in a subsurface ground enclosure has an electronic transmitter, an antenna and other circuitry that are encapsulated against moisture conditions. The assembly (10) comprises a circuit board (31) supporting radio frequency transmitter circuitry, the circuit board (31) having at least two orthogonal edges (31b, 31c), an antenna (14, 15) having two 90-degree bent portions extending from orthogonal edges (31b, 31c) of the circuit board (31), a housing bottom portion (11) with an internal barrier (18) separating an L-shaped antenna compartment (13) adjacent two sides of the circuit board (31) from a battery compartment (16) underneath the circuit board (31), sealing material for encapsulating the circuit board (31) and the batteries (17) in the battery compartment (16), and a cover (20) that protects the antenna compartment (13) from entry of the sealing material.

Abstract: Automated malware signature generation is disclosed. Automated malware signature generation includes monitoring incoming unknown files for the presence of malware and analyzing the incoming unknown files based on both a plurality of classifiers of file behavior and a plurality of classifiers of file content. An incoming file is classified as having a particular malware classification based on the analyzing of incoming unknown files and a malware signature is generated for the incoming unknown file based on the particular malware classification. Access is provided to the malware signature.

Abstract: A system and method for uploading data from a customer system to a hosted system is disclosed. A stub is integrated with a firewall between the customer system and the hosted system. The stub includes an inbound layer on the customer system side of the firewall and an outbound layer on the hosted system side of the firewall, and the inbound layer includes a write-only directory. A demon is connected between the inbound layer and the outbound layer of the stub. The demon is configured to recognize newly received data in the write-only directory of the inbound layer, encrypt the newly received data to generate encrypted data, and move the encrypted data to the outbound layer for access by the hosted system.

Abstract: An assembly (10) for installation in a subsurface ground enclosure has an electronic transmitter, an antenna and other circuitry that are encapsulated against moisture conditions. The assembly (10) comprises a circuit board (31) supporting radio frequency transmitter circuitry, the circuit board (31) having at least two orthogonal edges (31b, 31c), an antenna (14, 15) having two 90-degree bent portions extending from orthogonal edges (31b, 31c) of the circuit board (31), a housing bottom portion (11) with an internal barrier (18) separating an L-shaped antenna compartment (13) adjacent two sides of the circuit board (31) from a battery compartment (16) underneath the circuit board (31), sealing material for encapsulating the circuit board (31) and the batteries (17) in the battery compartment (16), and a cover (20) that protects the antenna compartment (13) from entry of the sealing material.

Abstract: Stack buffer overrun situations may be handled by a computer program that checks the memory location from where a particular function is called. As long as the return address for the function call is from a memory location of a known library that is loaded in memory, normal operation continues. If the memory location is not from a known library, the function call is suspect and execution may be terminated, since such a location may cause malicious software to be executed or abnormal program execution to happen. The memory location may also be verified by additional means, including testing whether the memory page permissions permit execution. The computer program may be a plug-in to an existing application and may also have a user-editable component. The computer program can enable a quick deployment of a temporary fix to a malicious software problem before a more permanent solution may be deployed.

Abstract: Automated malware signature generation is disclosed. Automated malware signature generation includes monitoring incoming unknown files for the presence of malware and analyzing the incoming unknown files based on both a plurality of classifiers of file behavior and a plurality of classifiers of file content. An incoming file is classified as having a particular malware classification based on the analyzing of incoming unknown files and a malware signature is generated for the incoming unknown file based on the particular malware classification. Access is provided to the malware signature.