International audienceThe AVANTSSAR Platform is an integrated toolset for the formal specification and automated validation of trust and security of service-oriented architectures and other applications in the Internet of Services. The platform supports application-level specification languages (such as BPMN and our custom languages) and features three validation backends (CL-AtSe, OFMC, and SATMC), which provide a range of complementary automated reasoning techniques (including service orchestration, compositional reasoning, model checking, and abstract interpretation). We have applied the platform to a large number of industrial case studies, collected into the AVANTSSAR Library of validated problem cases. In doing so, we unveiled a number of problems and vulnerabilities in deployed services. These include, most notably, a serious flaw in the SAML-based Single Sign-On for Google Apps (now corrected by Google as a result of our findings). We also report on the migration of the platform to industry
Behavioral and targeted profiling of users is an important task in marketing and in the advertising industry. Being able to match a given user profile to an advertising that leads to effective purchases is challenging because of a very tiny proportion of users willing to purchase goods and thus monetize the advertising. With such proportions being less than one percent of the overall user population, efficient feature extraction and modeling techniques are required in order to capture and recognize the potential consumers. This paper proposes a new approach for modeling the observed behavior in a mobile advertising platform, where time related features are correlated with additional system level and campaign related performance statistics. We capture the temporal behavior with Hawkes processes and use the estimated parameters as additional features for predicting if a given user profile will be a revenue generating customer.
Abstract-Software-defined deployments are growing into data center and enterprise network infrastructures. The typical promises of software-defined networks (SDN) are improved time for market, decreased risk and operational costs for services, flexibility and unified management. However, little is known and shared about how to actually manage an SDN network, especially in localising underperforming network paths (what we call "troubleshooting"). We describe a novel approach to ease large network troubleshooting by leveraging SDN features and incorporating distributed monitoring of network traffic. We suggest SDN-RADAR, a tool that can help network administrators understand which is the most likely faulty network link. To the best of our knowledge this is the first troubleshooting solution that combines user-side performance measurements with network data extracted from the SDN controller.
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