Abstract: A method for identifying a network application. The method includes analyzing metadata and source code of a network application to extract a set of application tokens, generating an index document of the network application based on the set of application code tokens, wherein the index document is included in a library of index documents corresponding to a number of network applications, extracting a set of packet header tokens from a packet header of a packet in a flow, comparing the set of packet header tokens to the set of index documents to generate a number of match scores, wherein each match score represents a similarity measure between the set of packet header tokens and one index document, and determining, based on a highest match score corresponding to a particular network application, that the flow is generated by the particular network application.

Abstract: The disclosed computer-implemented method for creating automatic computer-generated classifications may include (i) mining webpages of entities with a known classification, (ii) using information mined from the webpages to create a classification structure that assigns class labels to entities based on entity webpage content, (iii) applying, to the classification structure, one or more webpages of a new entity with an unknown classification, (iv) receiving, from the classification structure, a class label for the new entity, and (v) performing a security action based on the new entity's class label. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for automated classification of application network activity may include (1) building a lexicon dictionary that comprises lexical keywords, wherein network streams whose headers contain a given lexical keyword represent communications of an activity type that is associated with the given lexical keyword in the lexicon dictionary, (2) identifying, at a network vantage point, a network stream that represents a communication between an application and a server, (3) extracting, through a lexical analysis that utilizes the lexicon dictionary, a set of keywords from one or more header fields of the network stream, and (4) classifying the network stream based on activity types associated with each keyword in the set of keywords that were extracted from the header fields of the network stream. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for providing computing security by classifying organizations may include (1) identifying a request to classify an organization, (2) generating a web page dynamically in response to the request, the web page including content that describes the organization, (3) locating, within the web page, an advertisement that is dynamically generated by an advertisement network based at least in part on the content of the web page, (4) analyzing the advertisement to determine an organizational classification associated with the advertisement, and (5) imputing the organizational classification to the organization based at least in part on the advertisement having been dynamically generated by the advertisement network based at least in part on the content of the web page. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A method for identifying a network application. The method includes analyzing metadata and source code of a network application to extract a set of application tokens, generating an index document of the network application based on the set of application code tokens, wherein the index document is included in a library of index documents corresponding to a number of network applications, extracting a set of packet header tokens from a packet header of a packet in a flow, comparing the set of packet header tokens to the set of index documents to generate a number of match scores, wherein each match score represents a similarity measure between the set of packet header tokens and one index document, and determining, based on a highest match score corresponding to a particular network application, that the flow is generated by the particular network application.

Abstract: A method for identifying a network application. The method includes analyzing metadata and source code of a network application to extract a set of application tokens, generating an index document of the network application based on the set of application code tokens, wherein the index document is included in a library of index documents corresponding to a number of network applications, extracting a set of packet header tokens from a packet header of a packet in a flow, comparing the set of packet header tokens to the set of index documents to generate a number of match scores, wherein each match score represents a similarity measure between the set of packet header tokens and one index document, and determining, based on a highest match score corresponding to a particular network application, that the flow is generated by the particular network application.

Abstract: A computer-implemented method for determining security risk profiles may include (1) detecting a security breach of an entity within a set of entities, (2) constructing a peer-similarity graph that identifies an incentive to attack the entity in comparison to other entities within the set of entities, (3) creating, using the peer-similarity graph, a security risk profile for each entity in the set of entities, (4) automatically adjusting at least one security risk profile based on the detected security breach, and (5) updating a security database with the adjusted security risk profile. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for automated classification of application network activity may include (1) building a lexicon dictionary that comprises lexical keywords, wherein network streams whose headers contain a given lexical keyword represent communications of an activity type that is associated with the given lexical keyword in the lexicon dictionary, (2) identifying, at a network vantage point, a network stream that represents a communication between an application and a server, (3) extracting, through a lexical analysis that utilizes the lexicon dictionary, a set of keywords from one or more header fields of the network stream, and (4) classifying the network stream based on activity types associated with each keyword in the set of keywords that were extracted from the header fields of the network stream. Various other methods, systems, and computer-readable media are also disclosed.