Abstract: Techniques for connecting known entities to a protected network are described. A user device with a certified application installed is authenticated with an identification repository. The authentication is accomplished using credentials associated with the certified application. The user device is also enrolled with an authentication server and the authenticated user device is connected to the protected network.

Abstract: A server obtains security intelligence data used for classifying whether data associated with user activity in a network is undesirable, and classifies the data based on the security intelligence data. The server provides an initial classifying result of the data to a device associated with the data. At a subsequent time, the server obtains updated security intelligence data and re-classifies whether the first data is undesirable based on the updated security intelligence data. Responsive to a determination that the initial classifying result is changed based on the re-classifying, the server provides an updated classifying result to the device associated with the data.

Abstract: A server obtains security intelligence data used for classifying whether data associated with user activity in a network is undesirable, and classifies the data based on the security intelligence data. The server provides an initial classifying result of the data to a device associated with the data. At a subsequent time, the server obtains updated security intelligence data and re-classifies whether the first data is undesirable based on the updated security intelligence data. Responsive to a determination that the initial classifying result is changed based on the re-classifying, the server provides an updated classifying result to the device associated with the data.

Abstract: Techniques for connecting known entities to a protected network are described. A user device with a certified application installed is authenticated with an identification repository. The authentication is accomplished using credentials associated with the certified application. The user device is also enrolled with an authentication server and the authenticated user device is connected to the protected network.

Abstract: A method includes: at a server, obtaining security intelligence data used for classifying whether a data associated with a user activity in a network is undesirable at a first time; classifying whether a first data in the network is undesirable based on the security intelligence data; receiving a request for classifying whether a second data is undesirable based on the security intelligence data; determining whether the server is overloaded with tasks; if the server is determined to be overloaded with tasks: logging the second data in a repository, and tagging the second data to re-visit classification of the second data; and when the server is no longer overloaded, classifying whether the second data is undesirable to produce a second classifying result and re-classifying whether the first data is undesirable based on updated security intelligence data obtained by the server.

Abstract: A method includes: at a server, obtaining security intelligence data used for classifying whether a data associated with a user activity in a network is undesirable at a first time; classifying whether a first data in the network is undesirable based on the security intelligence data; receiving a request for classifying whether a second data is undesirable based on the security intelligence data; determining whether the server is overloaded with tasks; if the server is determined to be overloaded with tasks: logging the second data in a repository, and tagging the second data to re-visit classification of the second data; and when the server is no longer overloaded, classifying whether the second data is undesirable to produce a second classifying result and re-classifying whether the first data is undesirable based on updated security intelligence data obtained by the server.

Abstract: Presented herein are vulnerability assessment techniques for highlighting an organization's information technology (IT) infrastructure security vulnerabilities. For example, a vulnerability assessment system obtains application metadata for each of a plurality of executable applications observed at one or more devices forming part of an organization's IT infrastructure. The application metadata includes unique software identifiers for each of the plurality of executable applications. The vulnerability assessment system obtains global security risk metadata for executable applications observed at the one or more devices. The vulnerability assessment system maps one or more unique software identifiers in the application metadata to global security risk metadata that corresponds to applications identified by the one or more unique software identifiers, thereby generating a vulnerable application dataset.

Abstract: A system retroactively detects malicious software on an end user system without performing expensive cross-referencing directly on the endpoint device. A client provides a server with information about files that are on it together with what it knows about these files. The server tracks this information and cross-references it against new intelligence it gathers on clean or malicious files. If a discrepancy in found (i.e., a file that had been called malicious, but that is actually benign or vice versa), the server informs the client, which in turn takes an appropriate action based on this information.

Abstract: Techniques are presented herein that combine a host-based analysis of an executable file on a host computer with a network-based analysis, i.e., an analysis of domain names to detect malware generated domain names that are used by the malicious executable files to establish malicious network connections. A server receives information from a host computer about an executable file that, when executed on the host computer, initiates a network connection. The server also receives information about the network connection itself. The server analyzes the information about the executable file to determine whether the executable file has a malicious disposition. Depending on a disposition of the executable file, the server analyzes the information about the network connection and determines whether the network connection is malicious.

Abstract: Techniques are presented herein that combine a host-based analysis of an executable file on a host computer with a network-based analysis, i.e., an analysis of domain names to detect malware generated domain names that are used by the malicious executable files to establish malicious network connections. A server receives information from a host computer about an executable file that, when executed on the host computer, initiates a network connection. The server also receives information about the network connection itself. The server analyzes the information about the executable file to determine whether the executable file has a malicious disposition. Depending on a disposition of the executable file, the server analyzes the information about the network connection and determines whether the network connection is malicious.

Abstract: Presented herein are vulnerability assessment techniques for highlighting an organization's information technology (IT) infrastructure security vulnerabilities. For example, a vulnerability assessment system obtains application metadata for each of a plurality of executable applications observed at one or more devices forming part of an organization's IT infrastructure. The application metadata includes unique software identifiers for each of the plurality of executable applications. The vulnerability assessment system obtains global security risk metadata for executable applications observed at the one or more devices. The vulnerability assessment system maps one or more unique software identifiers in the application metadata to global security risk metadata that corresponds to applications identified by the one or more unique software identifiers, thereby generating a vulnerable application dataset.

Abstract: Novel methods, components, and systems for detecting malicious software in a proactive manner are presented. More specifically, we describe methods, components, and systems that leverage machine learning techniques to detect malicious software. The disclosed invention provides a significant improvement with regard to detection capabilities compared to previous approaches.

Abstract: Novel methods, components, and systems for automatically detecting malicious software are presented. More specifically, methods, components, and systems for the automated deployment of generic signatures to detect malicious software. Even more specifically, computer implemented methods for determining whether a software application is likely malicious including computing at a client component a generic fingerprint for a software application, transmitting the generic fingerprint data to a server component, receiving at the client component information from the server component relating to the generic fingerprint of the software application, and following a prescribed set of actions based on the information received from the server.

Abstract: A system retroactively detects malicious software on an end user system without performing expensive cross-referencing directly on the endpoint device. A client provides a server with information about files that are on it together with what it knows about these files. The server tracks this information and cross-references it against new intelligence it gathers on clean or malicious files. If a discrepancy in found (i.e., a file that had been called malicious, but that is actually benign or vice versa), the server informs the client, which in turn takes an appropriate action based on this information.

Abstract: Novel methods, components, and systems that enhance traditional techniques for detecting malicious software are presented. More specifically, methods, components, and systems that use important contextual information from a client system (such as recent history of events on that system), machine learning techniques, the automated deployment of generic signatures, and combinations thereof, to detect malicious software. The disclosed invention provides a significant improvement with regard to automation compared to previous approaches.

Abstract: A system for retroactively detecting malicious software on an end user system without performing expensive cross-referencing directly on the endpoint device. A client provides a server with information about files that are on it together with what it knows about these files. The server tracks this information and cross-references it against new intelligence it gathers on clean or malicious files. If a discrepancy is found (i.e., a file that had been called malicious, but that is actually benign or vice versa), the server informs the client, which in turn takes an appropriate action based on this information.

Abstract: Novel methods, components, and systems for detecting malicious software in a proactive manner are presented. More specifically, we describe methods, components, and systems that leverage machine learning techniques to detect malicious software. The disclosed invention provides a significant improvement with regard to detection capabilities compared to previous approaches.

Abstract: Novel methods, components, and systems for detecting malicious software in a proactive manner are presented. More specifically, we describe methods, components, and systems that leverage machine learning techniques to detect malicious software. The disclosed invention provides a significant improvement with regard to detection capabilities compared to previous approaches.

Abstract: A system for retroactively detecting malicious software on an end user system without performing expensive cross-referencing directly on the endpoint device. A client provides a server with information about files that are on it together with what it knows about these files. The server tracks this information and cross-references it against new intelligence it gathers on clean or malicious files. If a discrepancy is found (i.e., a file that had been called malicious, but that is actually benign or vice versa), the server informs the client, which in turn takes an appropriate action based on this information.

Abstract: Novel methods, components, and systems that enhance traditional techniques for detecting malicious software are presented. More specifically, we describe methods, components, and systems that leverage important contextual information from a client system (such as recent history of events on that system) to detect malicious software that might have otherwise gone ignored. The disclosed invention provides a significant improvement with regard to detection capabilities compared to previous approaches.