M
Max Mühlhäuser
Researcher at Technische Universität Darmstadt
Publications - 729
Citations - 8807
Max Mühlhäuser is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Computer science & Context (language use). The author has an hindex of 39, co-authored 690 publications receiving 7390 citations. Previous affiliations of Max Mühlhäuser include Karlsruhe Institute of Technology & Electronics and Telecommunications Research Institute.
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Taxonomy and Survey of Collaborative Intrusion Detection
TL;DR: The entire framework of requirements, building blocks, and attacks as introduced is used for a comprehensive analysis of the state of the art in collaborative intrusion detection, including a detailed survey and comparison of specific CIDS approaches.
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Towards a Trust Management System for Cloud Computing
TL;DR: A multi-faceted Trust Management (TM) system architecture is proposed that provides means to identify the trustworthy cloud providers in terms of different attributes assessed by multiple sources and roots of trust information.
Proceedings ArticleDOI
Service Entity Placement for Social Virtual Reality Applications in Edge Computing
TL;DR: This paper proposes ITEM, an iterative algorithm with fast and big “moves” where in each iteration, a graph is constructed to encode all the costs and convert the cost optimization into a graph cut problem, and can simultaneously determine the placement of multiple service entities.
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Trust as a facilitator in cloud computing: a survey
TL;DR: This work contributes to understanding why trust establishment is important in the Cloud computing landscape, how trust can act as a facilitator in this context and what are the exact requirements for trust and reputation models (or systems) to support the consumers in establishing trust on Cloud providers.
Proceedings Article
A Multi-Indicator Approach for Geolocalization of Tweets
TL;DR: This work proposes the first multi-indicator method for determining the location where a tweet was created as well as the location of the user's residence, based on various weighted indicators, including the names of places that appear in the text message, dedicated location entries, and additional information from the user profile.