T
Thomas Bauschert
Researcher at Chemnitz University of Technology
Publications - 101
Citations - 854
Thomas Bauschert is an academic researcher from Chemnitz University of Technology. The author has contributed to research in topics: Network topology & Network architecture. The author has an hindex of 12, co-authored 92 publications receiving 616 citations. Previous affiliations of Thomas Bauschert include Nokia Networks & Technische Universität München.
Papers
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Proceedings ArticleDOI
Mobile core network virtualization: A model for combined virtual core network function placement and topology optimization
TL;DR: A novel integer linear programming formulation which combines the optimization of the virtual network topology with VNE optimization and its embedding onto a physical substrate network is proposed and it is shown that this approach outperforms traditional V NE optimization approaches in terms of optimality and computation time.
Journal ArticleDOI
Network planning under demand uncertainty with robust optimization
Thomas Bauschert,Christina Büsing,Fabio D'Andreagiovanni,Arie M. C. A. Koster,Manuel Kutschka,Uwe Steglich +5 more
TL;DR: This article shows by example how the emerging area of robust optimization can advance the network planning by a more accurate mathematical description of the demand uncertainty by presenting two applications: multi-layer and mixed-line-rate network design.
Proceedings ArticleDOI
Combined Virtual Mobile Core Network Function Placement and Topology Optimization with Latency Bounds
TL;DR: A novel mathematical optimization model for virtual mobile core network embeddings with respect to latency bounds is presented and upper bounds for the latency caused by processing, packet queueing and propagation are considered.
Journal ArticleDOI
Securing Heterogeneous IoT With Intelligent DDoS Attack Behavior Learning
Nhu-Ngoc Dao,Trung V. Phan,Umar Sa'ad,Joongheon Kim,Thomas Bauschert,Dinh-Thuan Do,Sungrae Cho +6 more
TL;DR: This study proposes MECshield, a localized DDoS prevention framework leveraging MEC power to deploy multiple smart filters at the edge of relevant attack-source/destination networks that outperforms existing solutions.
Journal ArticleDOI
DeepGuard: Efficient Anomaly Detection in SDN With Fine-Grained Traffic Flow Monitoring
Trung V. Phan,Tri Gia Nguyen,Nhu-Ngoc Dao,Truong Thu Huong,Nguyen Huu Thanh,Thomas Bauschert +5 more
TL;DR: An efficient anomaly detection framework, denoted as DeepGuard, is proposed, which improves the detection performance of cyberattacks in SDN based networks by adopting a fine-grained traffic flow monitoring mechanism.