@inproceedings{SchubaHoefkenLinzbach2022,
  author    = {Schuba, Marko and H{\"o}fken, Hans-Wilhelm and Linzbach, Sophie},
  title     = {An ICS Honeynet for Detecting and Analyzing Cyberattacks in Industrial Plants},
  series = {2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)},
  booktitle = {2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)},
  publisher = {IEEE},
  isbn      = {978-1-6654-4231-2},
  doi       = {10.1109/ICECET52533.2021.9698746},
  pages     = {6 Seiten},
  year      = {2022},
  abstract  = {Cybersecurity of Industrial Control Systems (ICS) is an important issue, as ICS incidents may have a direct impact on safety of people or the environment. At the same time the awareness and knowledge about cybersecurity, particularly in the context of ICS, is alarmingly low. Industrial honeypots offer a cheap and easy to implement way to raise cybersecurity awareness and to educate ICS staff about typical attack patterns. When integrated in a productive network, industrial honeypots may not only reveal attackers early but may also distract them from the actual important systems of the network. Implementing multiple honeypots as a honeynet, the systems can be used to emulate or simulate a whole Industrial Control System. This paper describes a network of honeypots emulating HTTP, SNMP, S7communication and the Modbus protocol using Conpot, IMUNES and SNAP7. The nodes mimic SIMATIC S7 programmable logic controllers (PLCs) which are widely used across the globe. The deployed honeypots' features will be compared with the features of real SIMATIC S7 PLCs. Furthermore, the honeynet has been made publicly available for ten days and occurring cyberattacks have been analyzed},
  language  = {en}
}
@inproceedings{NowackRoethBuehrigPolaczeketal.2008,
  author    = {Nowack, N. and R{\"o}th, Thilo and B{\"u}hrig-Polaczek, A. and Klaus, G.},
  title     = {Advanced Sheet Metal Components Reinforced by Light Metal Cast Structures},
  series = {Aluminium alloys : their physical and mechanical properties ; [proceedings of the 11th International Conference on Aluminium Alloys, 22 - 26 Sept. 2008, Aachen, Germany ; ICAA 11]},
  booktitle = {Aluminium alloys : their physical and mechanical properties ; [proceedings of the 11th International Conference on Aluminium Alloys, 22 - 26 Sept. 2008, Aachen, Germany ; ICAA 11]},
  number    = {2},
  editor    = {Hirsch, J{\"u}rgen},
  isbn      = {978-3-527-32367-8},
  pages     = {2374 -- 2381},
  year      = {2008},
  language  = {en}
}
@inproceedings{SchulzeMuehleisenFeyerl2018,
  author    = {Schulze, Sven and M{\"u}hleisen, M. and Feyerl, G{\"u}nter},
  title     = {Adaptive energy management strategy for a heavy-duty truck with a P2-hybrid topology},
  series = {18. Internationales Stuttgarter Symposium. Proceedings},
  booktitle = {18. Internationales Stuttgarter Symposium. Proceedings},
  publisher = {Springer Vieweg},
  address   = {Wiesbaden},
  doi       = {10.1007/978-3-658-21194-3},
  pages     = {75 -- 89},
  year      = {2018},
  language  = {en}
}
@inproceedings{Elsen1998,
  author    = {Elsen, Ingo},
  title     = {A pixel based approach to view based object recognition with self-organizing neural networks},
  series = {IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society},
  booktitle = {IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {0-7803-4503-7},
  doi       = {10.1109/IECON.1998.724032},
  pages     = {2040 -- 2044},
  year      = {1998},
  abstract  = {This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images.},
  language  = {en}
}
@inproceedings{KreyerMuellerEsch2020,
  author    = {Kreyer, J{\"o}rg and M{\"u}ller, Marvin and Esch, Thomas},
  title     = {A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude},
  series = {Deutscher Luft- und Raumfahrtkongress 2019, „Luft- und Raumfahrt - technologische Br{\"u}cke in die Zukunft", Darmstadt, 30. September bis 2. Oktober 2019},
  booktitle = {Deutscher Luft- und Raumfahrtkongress 2019, „Luft- und Raumfahrt - technologische Br{\"u}cke in die Zukunft", Darmstadt, 30. September bis 2. Oktober 2019},
  publisher = {Deutsche Gesellschaft f{\"u}r Luft- und Raumfahrt - Lilienthal-Oberth e.V},
  address   = {Bonn},
  doi       = {10.25967/490162},
  pages     = {13 Seiten},
  year      = {2020},
  language  = {en}
}
@inproceedings{FingerdeVriesVosetal.2020,
  author    = {Finger, Felix and de Vries, Reynard and Vos, Roelof and Braun, Carsten and Bil, Cees},
  title     = {A comparison of hybrid-electric aircraft sizing methods},
  series = {AIAA Scitech 2020 Forum},
  booktitle = {AIAA Scitech 2020 Forum},
  doi       = {10.2514/6.2020-1006},
  pages     = {31 Seiten},
  year      = {2020},
  language  = {en}
}
@inproceedings{WalterElsenMuelleretal.1999,
  author    = {Walter, Peter and Elsen, Ingo and M{\"u}ller, Holger and Kraiss, Karl-Friedrich},
  title     = {3D object recognition with a specialized mixtures of experts architecture},
  series = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings},
  booktitle = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {0-7803-5529-6},
  issn      = {1098-7576},
  doi       = {10.1109/IJCNN.1999.836243},
  pages     = {3563 -- 3568},
  year      = {1999},
  abstract  = {Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.},
  language  = {en}
}