@misc{ScharfenbergMottokArtmannetal., author = {Scharfenberg, Georg and Mottok, J{\"u}rgen and Artmann, Christina and Hobelsberger, Martin and Paric, Ivan and Großmann, Benjamin and Pohlt, Clemens and Wackerbarth, Alena and Pausch, Uli and Heidrich, Christiane and Fadanelli, Martin and Elsner, Michael and P{\"o}cher, Daniel and Pittroff, Lenz and Beer, Stefan and Br{\"u}ckl, Oliver and Haslbeck, Matthias and Sterner, Michael and Thema, Martin and Muggenthaler, Nicole and Lenck, Thorsten and G{\"o}tz, Philipp and Eckert, Fabian and Deubzer, Michael and Stingl, Armin and Simsek, Erol and Kr{\"a}mer, Stefan and Großmann, Benjamin and Schlegl, Thomas and Niedersteiner, Sascha and Berlehner, Thomas and Joblin, Mitchell and Mauerer, Wolfgang and Apel, Sven and Siegmund, Janet and Riehle, Dirk and Weber, Joachim and Palm, Christoph and Zobel, Martin and Al-Falouji, Ghassan and Prestel, Dietmar and Scharfenberg, Georg and Mandl, Roland and Deinzer, Arnulf and Halang, W. and Margraf-Stiksrud, Jutta and Sick, Bernhard and Deinzer, Renate and Scherzinger, Stefanie and Klettke, Meike and St{\"o}rl, Uta and Wiech, Katharina and Kubata, Christoph and Sindersberger, Dirk and Monkman, Gareth J. and Dollinger, Markus and Dembianny, Sven and K{\"o}lbl, Andreas and Welker, Franz and Meier, Matthias and Thumann, Philipp and Swidergal, Krzysztof and Wagner, Marcus and Haug, Sonja and Vernim, Matthias and Seidenst{\"u}cker, Barbara and Weber, Karsten and Arsan, Christian and Schone, Reinhold and M{\"u}nder, Johannes and Schroll-Decker, Irmgard and Dillinger, Andrea Elisabeth and Fuchshofer, Rudolf and Monkman, Gareth J. and Shamonin (Chamonine), Mikhail and Geith, Markus A. and Koch, Fabian and {\"U}hlin, Christian and Schratzenstaller, Thomas and Saßmannshausen, Sean Patrick and Auchter, Eberhard and Kriz, Willy and Springer, Othmar and Thumann, Maria and Kusterle, Wolfgang and Obermeier, Andreas and Udalzow, Anton and Schmailzl, Anton and Hierl, Stefan and Langer, Christoph and Schreiner, Rupert}, title = {Forschungsbericht 2015}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-00-048589-3}, doi = {10.35096/othr/pub-1386}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13867}, language = {de} } @article{WeberRinkeAlwardt, author = {Weber, Karsten and Rinke, Bernhard and Alwardt, Christian}, title = {Dr. Seltsam, oder wie ich lernte, die Drohne zu lieben}, series = {TATuP - Zeitschrift f{\"u}r Technikfolgenabsch{\"a}tzung in Theorie und Praxis}, volume = {27}, journal = {TATuP - Zeitschrift f{\"u}r Technikfolgenabsch{\"a}tzung in Theorie und Praxis}, number = {3}, publisher = {Institute for Technology Assessment and Systems Analysis / KIT}, doi = {10.14512/tatup.27.3.11}, pages = {11 -- 13}, abstract = {Drohnen werden inzwischen in vielen und sehr unterschiedlichen Kon-texten verwendet. Aus dem Blickwinkel der Technikfolgenabsch{\"a}tzung (TA) scheint es daher sinnvoll, den Umfang der momentanen und zu-k{\"u}nftigen Nutzung von Drohnen und daraus resultierende Implikatio-nen n{\"a}her zu beleuchten und eine Bestandsaufnahme durchzuf{\"u}hren. Dar{\"u}ber hinaus sollen die voraussichtlichen Pfade der weiteren Tech-nikentwicklung, relevante Akteure und deren Interessenslage sowie zu-k{\"u}nftige Anwendungspotenziale und Einsatzfelder analysiert werden}, language = {de} } @article{HinterbergerFischerWeberetal., author = {Hinterberger, Lukas and Fischer, Sebastian and Weber, Bernhard and Neubauer, Katrin and Hackenberg, Rudolf}, title = {Extended Definition of the Proposed Open Standard for IoT Device IdentificAtion and RecoGnition (IoTAG)}, series = {International Journal on Advances in Internet Technology}, volume = {13}, journal = {International Journal on Advances in Internet Technology}, number = {3 \& 4}, publisher = {IARIA}, pages = {110 -- 121}, abstract = {Internet of Things (IoT) devices are critical to operate and maintain, because of their number and high connectivity. A lot of security issues concern IoT devices and the networks they are integrated. To help getting an overview of an IoT network, the devices and the security, we propose a scoring system to get a good impression of IT security. This system generates single scores for each device, using features like encryption, update behavior, etc. Furthermore, a summarized score for the whole network is calculated, to show the status of the network security in an easy way for the administrator. To enable the scoring system, a precise list of the existing devices and their operating status is necessary. To achieve this, we present an open standard for the IoT Device IdentificAtion and RecoGnition (short IoTAG), which requires that devices report, e.g., their name, an unique ID, the firmware version and the supported encryption. The proposed standard is described in detail and an implementation guideline is given in this paper. Additionally, information about how to realize the serialization, the integrity and the communication with IoTAG.}, language = {en} } @inproceedings{HinterbergerWeberFischeretal., author = {Hinterberger, Lukas and Weber, Bernhard and Fischer, Sebastian and Neubauer, Katrin and Hackenberg, Rudolf}, title = {IoT Device IdentificAtion and RecoGnition (IoTAG)}, series = {CLOUD COMPUTING 2020, The Eleventh International Conference on Cloud Computing, GRIDs and Virtualization}, booktitle = {CLOUD COMPUTING 2020, The Eleventh International Conference on Cloud Computing, GRIDs and Virtualization}, publisher = {IARIA}, isbn = {978-1-61208-778-8}, pages = {17 -- 23}, abstract = {To ensure the secure operation of IoT devices in the future, they must be continuously monitored. This starts with an inventory of the devices, checking for a current software version and extends to the encryption algorithms and active services used. Based on this information, a security analysis and rating of the whole network is possible. To solve this challenge in the growing network environments, we present a proposal for a standard. With the IoT Device IdentificAtion and RecoGnition (IoTAG), each IoT device reports its current status to a central location as required and provides information on security. This information includes a unique ID, the exact device name, the current software version, active services, cryptographic methods used, etc. The information is signed to make misuse more difficult and to ensure that the device can always be uniquely identified. In this paper, we introduce IoTAG in detail and describe the necessary requirements.}, language = {en} } @inproceedings{WeberHinterbergerFischeretal., author = {Weber, Bernhard and Hinterberger, Lukas and Fischer, Sebastian and Hackenberg, Rudolf}, title = {How to Prevent Misuse of IoTAG?}, series = {Cloud Computing 2021: the Twelfth International Conference on Cloud Computing, GRIDs, and Virtualization: April 18-22, 2021, Porto, Portugal}, booktitle = {Cloud Computing 2021: the Twelfth International Conference on Cloud Computing, GRIDs, and Virtualization: April 18-22, 2021, Porto, Portugal}, editor = {Duncan, Bob and Lee, Yong Woo and Popescu, Manuela}, publisher = {IARIA}, address = {Wilmington, DE, USA}, isbn = {978-1-61208-845-7}, issn = {2308-4294}, abstract = {Since IoT devices are potentially insecure and offer great attack potential, in our past research we presented IoTAG, a solution where devices communicate security-related information about themselves. However, since this information can also be exploited by attackers, we present in this paper a solution against the misuse of IoTAG. In doing so, we address the two biggest problems: authentication and pairing with a trusted device. This is solved by introducing a pairing process, which uses the simultaneous authentication of equals algorithm to securely exchange and verify each others signature, and by using the server and client authentication provided by HTTP over TLS. We provide the minimum requirements and evaluate the methods used. The emphasis is on known and already proven methods. Additionally, we analyze the potential consequences of an attacker tapping the IoTAG information. Finally, we conclude that the solution successfully prevents access to IoTAG by unauthorized clients on the same network.}, language = {en} } @book{ReichenbachBrussBuergeretal., author = {Reichenbach, Hartmut and Bruss, Werner and B{\"u}rger, Michael and Fiedler, Manfred and G{\"o}bel, Manuel and Middendorf, Bernhard and Neidhart, Thomas and Pollnow, Elke and Ramler-Kowollik, Marcel and Scholz, Enrico and Simon, Jana and Sosinka, Katharina and Spirkl, Florian and Trunk, Ulrich and Wagner, Achim and Wagner, Bernd and Weber, Steffen}, title = {Merkblatt f{\"u}r die Herstellung und Verwendung von zeitweise fließf{\"a}higen, selbstverdichtenden Verf{\"u}llbaustoffen (ZFSV) im Erdbau}, editor = {Forschungsgesellschaft f{\"u}r Straßen- und Verkehrswesen FGSV e.V.,}, edition = {Ausgabe 2025}, publisher = {FGSV Verlag}, address = {K{\"o}ln}, isbn = {978-3-86446-419-5}, pages = {67}, abstract = {Das „Merkblatt f{\"u}r die Herstellung und Verwendung von zeitweise fließf{\"a}higen, selbstverdichtenden Verf{\"u}llbaustoffen" (M ZFSV), Ausgabe 2025, befasst sich mit der Herstellung und der Anwendung zeitweise fließf{\"a}higer, selbstverdichtender Verf{\"u}llbaustoffe (ZFSV) gem{\"a}ß den „Zus{\"a}tzlichen Technischen Vertragsbedingungen und Richtlinien f{\"u}r Erdarbeiten im Straßenbau" (ZTV E-StB) (FGSV 599) beim Bau von Straßen und Verkehrsfl{\"a}chen. F{\"u}r andere Bereiche des Bauwesens kann dieses Merkblatt ebenfalls angewendet werden. Das M ZFSV enth{\"a}lt Qualit{\"a}tsanforderungen f{\"u}r diese Baustoffe und Baustoffgemische. Die Anforderungen schließen die verschiedenen Anwendungsgebiete der Baustoffe und Baustoffgemische, die Herstellung, Lieferung und den Einbau sowie die Pr{\"u}fungen und die Qualit{\"a}tssicherung ein. Vorab wird ebenso auf Anwendungsgebiete und Planungsgrunds{\"a}tze eingegangen. Erg{\"a}nzt werden die umfangreichen Ausf{\"u}hrungen durch mehrere Anh{\"a}nge.}, language = {de} } @article{GrassmannMengelkampBrandletal., author = {Graßmann, Felix and Mengelkamp, Judith and Brandl, Caroline and Harsch, Sebastian and Zimmermann, Martina E. and Linkohr, Birgit and Peters, Annette and Heid, Iris M. and Palm, Christoph and Weber, Bernhard H. F.}, title = {A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography}, series = {Ophtalmology}, volume = {125}, journal = {Ophtalmology}, number = {9}, publisher = {Elsevier}, doi = {10.1016/j.ophtha.2018.02.037}, pages = {1410 -- 1420}, abstract = {Purpose Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm. Design Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study. Participants. We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study. Methods. We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study. Main Outcome Measures. κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification. Results. A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92\% (95\% confidence interval, 89\%-92\%) and an overall accuracy of 63.3\%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50\% and 63\%, respectively. Importantly, the algorithm detected 84.2\% of all fundus images with definite signs of early or late AMD. Overall, 94.3\% of healthy fundus images were classified correctly. Conclusions Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age.}, subject = {Senile Makuladegeneration}, language = {en} }