[ { "title": "Analytics for application programming interfaces", "publication_date": "2013/07/11", "number": "09146787", "url": "/2013/11/07/analytics-for-application-programming-interfaces/", "abstract": "API analytics systems and methods are provided. Frequently occurring API usage patterns may be determined from API call data collected from across an API ecosystem. Alternatively or in addition, a classification structure, such as a decision tree, may be generated from the API usage patterns. A type of activity that resulted in a set of API calls being invoked may be determined from the classification structure. A similarity or difference between the set of API calls and the frequently occurring API usage patterns may also be determined and/or identified.", "owner": "Accenture Global Services Limited", "owner_city": "Dublin", "owner_country": "IE" }, { "title": "Method and apparatus for measuring the end-to-end performance and capacity of complex network service", "publication_date": "2013/10/04", "number": "09054970", "url": "/2013/04/10/method-and-apparatus-for-measuring-the-endtoend-performance-and-capacity-of-complex-network-service/", "abstract": "A method and system of measuring performance and capacity of a network includes monitoring network activity using an application programming interfaces (API) monitoring system with a web services definition language (WSDL) file and a probe. The WSDL file provides instructions to a probe control system in the API monitoring system and the probe control system provides regular expressions to the probe. Performance data are collected using the probe. The performance data includes a collection of performance information. Using the probe, performance data are transmitted between the network and the API monitoring system. The API monitoring system stores the performance data in a database of performance data monitored and analyzes the performance data to determine performance and capacity of the network. The API monitoring system reports information on performance and capacity of the network to a network operations center dashboard.", "owner": "AT&T INTELLECTUAL PROPERTY I, L.P.", "owner_city": "Atlanta", "owner_country": "US" }, { "title": "Execution profile assembly using branch records", "publication_date": "2013/05/03", "number": "09613212", "url": "/2013/03/05/execution-profile-assembly-using-branch-records2/", "abstract": "Technologies for assembling an execution profile of an event are disclosed. The system and method may include recording a plurality of branch records, generating a first test event substantially identical to the event, verifying legitimacy of an owner of a code segment associated with the event, establishing an initial point of an execution chain associated with the event, establishing a final point of the execution chain associated with the event, analyzing branch records for an address associated with the code segment, installing a plurality of primary monitors within the execution chain associated with the event, and triggering the plurality of primary monitors.", "owner": "McAfee, Inc.", "owner_city": "Santa Clara", "owner_country": "US" }, { "title": "Malware family identification using profile signatures", "publication_date": "2013/30/01", "number": "09165142", "url": "/2013/01/30/malware-family-identification-using-profile-signatures/", "abstract": "Techniques for malware family identification using profile signatures are disclosed. In some embodiments, malware identification using profile signatures includes executing a potential malware sample in a virtual machine environment (e.g., a sandbox); and determining whether the potential malware sample is associated with a known malware family based on a profile signature. In some embodiments, the virtual machine environment is an instrumented virtual machine environment for monitoring potential malware samples during execution.", "owner": "Palo Alto Networks, Inc.", "owner_city": "Santa Clara", "owner_country": "US" } ]