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We use statistical learning to estimate end-to-end QoS metrics from device statistics, collected from a server cluster and an OpenFlow network. The results from our testbed, which runs a video-on-demand service and a key-value store, demonstrate that the learned models can estimate QoS metrics like frame rate or response time with errors bellow 10% for a given client. Interestingly, we find that service-level...
In recent years, IT Service Providers have been rapidly transforming to an automated service delivery model. This is due to advances in technology and driven by the unrelenting market pressure to reduce cost and maintain quality. Tremendous progress has been made to date towards attainment of truly automated service delivery; that is, the ability to deliver the same service automatically using the...
This paper addresses the problem of detection of “Slow” Denial of Service attacks. The problem is particularly challenging in virtue of the reduced amount of bandwidth generated by the attacks. A novel detection method is presented, which analyzes specific spectral features of traffic over small time horizons. No packet inspection is required. Extrapolated data refer to real traffic traces, elaborated...
The fast evolving nature of modern cyber threats and network monitoring as well as the increasing interest in virtualization approaches for more complex network middlebox functionalities call for new, “software-defined”, solutions to virtualize and simplify the programming and deployment of online (stream-based) traffic analysis functions. StreaMon is based on a data-plane abstraction devised to scalably...
The use of the web increases day-by-day. User visits the pages and gives the feedback about the content of the web page. If a user is paying attention on the content of a web page, he spends more time in comparison to the other pages. User chooses some click on the particular page for search query content. These clicks are stored on server log file for measuring the web page importance on the basis...
The setting in which similarity querying of metric data is outsourced to a service provider. Users query the server for the most similar data objects and data is revealed only to trusted users and not to anyone else. The need for privacy may be due to the data being sensitive (eg. in medicine), valuable (eg. in astronomy) or otherwise confidential. In this work, image retrieval on metric data of outsourced...
Existing studies on the security of biometric authentication with template protection have considered the adversaries who obtain only protected templates. Since biometric authentication systems transmit data other than the protected templates, we need to consider how to secure biometric authentication systems against adversaries with those data. In this paper, we propose a classification of adversaries...
In recent years, due to the appealing features of cloud computing, large amount of data have been stored in the cloud. Although cloud based services offer many advantages, privacy and security of the sensitive data is a big concern. To mitigate the concerns, it is desirable to outsource sensitive data in encrypted form. Encrypted storage protects the data against illegal access, but it complicates...
The structure of the domain name is highly relevant for providing insights into the management, organization and operation of a given enterprise. Security assessment and network penetration testing are using information sourced from the DNS service in order to map the network, perform reconnaissance tasks, identify services and target individual hosts. Tracking the domain names used by popular Botnets...
Thanks to Rich Internet Applications (RIAs) with their enhanced interactivity, responsiveness and dynamicity, the user experience in the Web 2.0 is becoming more and more appealing and user-friendly. The dynamic nature of RIAs and the heterogeneous technologies, frameworks, communication models used for implementing them negatively affect their analyzability and understandability. Consequently, specific...
Performability control of IT systems still lacks theoretically well-founded approaches that fit well to enterprise system management solutions. We propose a methodology for designing compact qualitative, state-based predictive performability control that use instrumentation provided by typical system monitoring frameworks. We identify the main systemic insufficiencies of current monitoring tools that...
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