@techreport{ThomaLaarmannMerkensetal.2020, author = {Thoma, Andreas and Laarmann, Lukas and Merkens, Torsten and Franzke, Till and M{\"o}hren, Felix and Buttermann, Lilly and van der Weem, Dirk and Fischer, Maximilian and Misch, Philipp and B{\"o}hme, Mirijam and R{\"o}th, Thilo and Hebel, Christoph and Ritz, Thomas and Franke, Marina and Braun, Carsten}, title = {Entwicklung eines intermodalen Mobilit{\"a}tskonzeptes f{\"u}r die Pilotregion NRW/Rhein-Maas Euregio und Schaffung voller Kundenakzeptanz durch Transfer von Standards aus dem PKW-Bereich auf ein Flugtaxi : Schlussbericht : Projektakronym: SkyCab (Kategorie B) : Laufzeit in Monaten: 6 : Hauptthema: Kategorie B: Innovative Ideen mit Bezug zu UAS/Flugtaxis}, publisher = {FH Aachen}, address = {Aachen}, pages = {97 Seiten}, year = {2020}, language = {de} } @inproceedings{BroennerHoefkenSchuba2016, author = {Broenner, Simon and H{\"o}fken, Hans-Wilhelm and Schuba, Marko}, title = {Streamlining extraction and analysis of android RAM images}, series = {Proceedings of the 2nd international conference on information systems security and privacy}, booktitle = {Proceedings of the 2nd international conference on information systems security and privacy}, organization = {ICISSP International Conference on Information Systems Security and Privacy <2, 2016, Rome, Italy>}, isbn = {978-989-758-167-0}, doi = {10.5220/0005652802550264}, pages = {255 -- 264}, year = {2016}, language = {en} } @article{SerrorHackHenzeetal.2021, author = {Serror, Martin and Hack, Sacha and Henze, Martin and Schuba, Marko and Wehrle, Klaus}, title = {Challenges and Opportunities in Securing the Industrial Internet of Things}, series = {IEEE Transactions on Industrial Informatics}, volume = {17}, journal = {IEEE Transactions on Industrial Informatics}, number = {5}, publisher = {IEEE}, address = {New York}, issn = {1941-0050}, doi = {10.1109/TII.2020.3023507}, pages = {2985 -- 2996}, year = {2021}, language = {en} } @inproceedings{DinghoferHartung2020, author = {Dinghofer, Kai and Hartung, Frank}, title = {Analysis of Criteria for the Selection of Machine Learning Frameworks}, series = {2020 International Conference on Computing, Networking and Communications (ICNC)}, booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)}, publisher = {IEEE}, address = {New York, NY}, doi = {10.1109/ICNC47757.2020.9049650}, pages = {373 -- 377}, year = {2020}, abstract = {With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.}, language = {en} } @inproceedings{NikolovskiRekeElsenetal.2021, author = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan}, title = {Machine learning based 3D object detection for navigation in unstructured environments}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, isbn = {978-1-6654-7921-9}, doi = {10.1109/IVWorkshops54471.2021.9669218}, pages = {236 -- 242}, year = {2021}, abstract = {In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.}, language = {en} } @incollection{SchubaHoefken2022, author = {Schuba, Marko and H{\"o}fken, Hans-Wilhelm}, title = {Cybersicherheit in Produktion, Automotive und intelligenten Geb{\"a}uden}, series = {IT-Sicherheit - Technologien und Best Practices f{\"u}r die Umsetzung im Unternehmen}, booktitle = {IT-Sicherheit - Technologien und Best Practices f{\"u}r die Umsetzung im Unternehmen}, publisher = {Carl Hanser Verlag}, address = {M{\"u}nchen}, isbn = {978-3-446-47223-5}, doi = {10.3139/9783446473478.012}, pages = {193 -- 218}, year = {2022}, language = {de} } @incollection{EnglaenderKaminskiSchuba2022, author = {Engl{\"a}nder, Jacques and Kaminski, Lars and Schuba, Marko}, title = {Informationssicherheitsmanagement}, series = {Digitalisierungs- und Informationsmanagement}, booktitle = {Digitalisierungs- und Informationsmanagement}, publisher = {Springer Vieweg}, address = {Berlin}, isbn = {978-3-662-63757-9}, doi = {10.1007/978-3-662-63758-6_15}, pages = {373 -- 398}, year = {2022}, abstract = {Daten und Informationen sind die wichtigsten Ressourcen vieler Unternehmen und m{\"u}ssen daher entsprechend gesch{\"u}tzt werden. Getrieben durch die erh{\"o}hte Vernetzung von Informationstechnologie, die h{\"o}here Offenheit infolge datengetriebener Dienstleistungen und eine starke Zunahme an Datenquellen, r{\"u}cken die Gefahren von Informationsdiebstahl, -manipulation und -verlust in den Fokus von produzierenden Unternehmen. Auf dem Weg zum lern- und wandlungsf{\"a}higen Unternehmen kann dies zu einem großen Hindernis werden, da einerseits zu hohe Sicherheitsanforderungen neue Entwicklungen beschr{\"a}nken, andererseits wegen des Mangels an ausreichenden Informationssicherheitskonzepten Unternehmen weniger Innovationen wagen. Deshalb bedarf es individuell angepasster Konzepte f{\"u}r die Bereiche IT-Security, IT-Safety und Datenschutz f{\"u}r vernetzte Produkte, Produktion und Arbeitspl{\"a}tze. Bei der Entwicklung und Durchsetzung dieser Konzepte steht der Faktor Mensch im Zentrum aller {\"U}berlegungen. In diesem Kapitel wird dargestellt, wie der Faktor Mensch bei der Erstellung von Informationssicherheitskonzepten in verschiedenen Phasen zu beachten ist. Beginnend mit der Integration von Informationssystemen und damit verbundenen Sicherheitsmaßnahmen, {\"u}ber die Administration, bis hin zur Anwendung durch den Endnutzer, werden Methoden beschrieben, die den Menschen, verbunden mit seinem Mehrwert wie auch den Risiken, einschließen. Dabei werden sowohl Grundlagen aufgezeigt als auch Konzepte vorgestellt, mit denen Entscheider in der Unternehmens-IT Leitlinien f{\"u}r die Informationssicherheit festlegen k{\"o}nnen.}, language = {de} } @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{ChristianMontagSchubaetal.2018, author = {Christian, Esser and Montag, Tim and Schuba, Marko and Allhof, Manuel}, title = {Future critical infrastructure and security - cyberattacks on charging stations}, series = {31st International Electric Vehicle Symposium \& Exhibition and International Electric Vehicle Technology Conference (EVS31 \& EVTeC 2018)}, booktitle = {31st International Electric Vehicle Symposium \& Exhibition and International Electric Vehicle Technology Conference (EVS31 \& EVTeC 2018)}, publisher = {Society of Automotive Engineers of Japan (JSAE)}, address = {Tokyo}, isbn = {978-1-5108-9157-9}, pages = {665 -- 671}, year = {2018}, language = {en} } @inproceedings{GaldiHartungDugelay2017, author = {Galdi, Chiara and Hartung, Frank and Dugelay, Jean-Luc}, title = {Videos versus still images: Asymmetric sensor pattern noise comparison on mobile phones}, series = {Electronic Imaging}, booktitle = {Electronic Imaging}, publisher = {Society for Imaging Science and Technology}, address = {Springfield, Virginia}, issn = {2470-1173}, doi = {10.2352/ISSN.2470-1173.2017.7.MWSF-331}, pages = {100 -- 103}, year = {2017}, abstract = {Nowadays, the most employed devices for recoding videos or capturing images are undoubtedly the smartphones. Our work investigates the application of source camera identification on mobile phones. We present a dataset entirely collected by mobile phones. The dataset contains both still images and videos collected by 67 different smartphones. Part of the images consists in photos of uniform backgrounds, especially collected for the computation of the RSPN. Identifying the source camera given a video is particularly challenging due to the strong video compression. The experiments reported in this paper, show the large variation in performance when testing an highly accurate technique on still images and videos.}, language = {en} }