CIPSEC will be at IEEE Smartcomp 2018 (4th IEEE International Conference on Smart Computing ) represented by University of Darmstadt (TUD) and ATOS
IEEE SMARTCOMP 2018 is the 4th edition of the conference and will held in Taormina, Sicily after the success of the previous editions. SMARTCOMP 2018 will include smart computing innovations in all different technology aspects including pervasive/ubiquitous computing, cloud computing, sensor networks, internet of things, big data analytics, security and privacy, social computing, cognitive computing, cyber-physical systems with their validation within smart computing environments, and applications such as smart buildings, smart cities, smart grids, precision agriculture and other societal applications contributing to smart living.
Researches Neeraj Suri from TUD and Ruben Trapero from ATOS are authors of the paper "Reliable Monitoring of Cloud Services" accepted to be presented at this conference.
Cloud services offer powerful support for implementing various CIP solutions. To secure cloud services, a plethora of monitoring mechanisms have been proposed recently. The existing mechanisms generate monitoring results based on the collected monitoring information that is assumed to be reliable. In reality, the information collected by cloud monitors is susceptible to reliability issues (e.g., monitor malfunctions, data corruptions, or data tampering). How to obtain reliable monitoring information for performing cloud monitoring is still an open issue. In this paper, we propose Whetstone as a novel approach to address the particular gap where an effective approach of ascertaining the reliable value from a set of collected monitoring data is absent. To this end, Whetstone first introduces a statistical approach to filter defective data from the collected monitoring data set. Next, Whetstone develops an optimization approach to quantify the reliability of the collected data by leveraging the value deviation of collected data. Finally, Whetstone devises a weighted aggregation approach for generating the reliable value based on the obtained information. We evaluate the proposed approach with different experimental configurations. The experimental results demonstrate our approach’s efficacy of successfully generating the reliable value of the maximum likelihood for raw data sets.