TY - RPRT A1 - Thoma, Andreas A1 - Laarmann, Lukas A1 - Merkens, Torsten A1 - Franzke, Till A1 - Möhren, Felix A1 - Buttermann, Lilly A1 - van der Weem, Dirk A1 - Fischer, Maximilian A1 - Misch, Philipp A1 - Böhme, Mirijam A1 - Röth, Thilo A1 - Hebel, Christoph A1 - Ritz, Thomas A1 - Franke, Marina A1 - Braun, Carsten T1 - Entwicklung eines intermodalen Mobilitätskonzeptes für 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 Y1 - 2020 N1 - Förderkennzeichen BMVI 45UAS1027A-F PB - FH Aachen CY - Aachen ER - TY - CHAP A1 - Broenner, Simon A1 - Höfken, Hans-Wilhelm A1 - Schuba, Marko T1 - Streamlining extraction and analysis of android RAM images T2 - Proceedings of the 2nd international conference on information systems security and privacy Y1 - 2016 SN - 978-989-758-167-0 U6 - https://doi.org/10.5220/0005652802550264 SP - 255 EP - 264 ER - TY - JOUR A1 - Serror, Martin A1 - Hack, Sacha A1 - Henze, Martin A1 - Schuba, Marko A1 - Wehrle, Klaus T1 - Challenges and Opportunities in Securing the Industrial Internet of Things JF - IEEE Transactions on Industrial Informatics Y1 - 2021 U6 - https://doi.org/10.1109/TII.2020.3023507 SN - 1941-0050 VL - 17 IS - 5 SP - 2985 EP - 2996 PB - IEEE CY - New York ER - TY - CHAP A1 - Dinghofer, Kai A1 - Hartung, Frank T1 - Analysis of Criteria for the Selection of Machine Learning Frameworks T2 - 2020 International Conference on Computing, Networking and Communications (ICNC) N2 - 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. Y1 - 2020 U6 - https://doi.org/10.1109/ICNC47757.2020.9049650 N1 - 2020 International Conference on Computing, Networking and Communications (ICNC), 17-20 February 2020, Big Island, HI, USA SP - 373 EP - 377 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - 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. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - https://doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - CHAP A1 - Schuba, Marko A1 - Höfken, Hans-Wilhelm T1 - Cybersicherheit in Produktion, Automotive und intelligenten Gebäuden T2 - IT-Sicherheit - Technologien und Best Practices für die Umsetzung im Unternehmen Y1 - 2022 SN - 978-3-446-47223-5 SN - 978-3-446-47347-8 U6 - https://doi.org/10.3139/9783446473478.012 SP - 193 EP - 218 PB - Carl Hanser Verlag CY - München ER - TY - CHAP A1 - Engländer, Jacques A1 - Kaminski, Lars A1 - Schuba, Marko T1 - Informationssicherheitsmanagement T2 - Digitalisierungs- und Informationsmanagement N2 - Daten und Informationen sind die wichtigsten Ressourcen vieler Unternehmen und müssen daher entsprechend geschützt werden. Getrieben durch die erhöhte Vernetzung von Informationstechnologie, die höhere Offenheit infolge datengetriebener Dienstleistungen und eine starke Zunahme an Datenquellen, rücken die Gefahren von Informationsdiebstahl, -manipulation und -verlust in den Fokus von produzierenden Unternehmen. Auf dem Weg zum lern- und wandlungsfähigen Unternehmen kann dies zu einem großen Hindernis werden, da einerseits zu hohe Sicherheitsanforderungen neue Entwicklungen beschränken, andererseits wegen des Mangels an ausreichenden Informationssicherheitskonzepten Unternehmen weniger Innovationen wagen. Deshalb bedarf es individuell angepasster Konzepte für die Bereiche IT-Security, IT-Safety und Datenschutz für vernetzte Produkte, Produktion und Arbeitsplätze. Bei der Entwicklung und Durchsetzung dieser Konzepte steht der Faktor Mensch im Zentrum aller Überlegungen. 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, über 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 für die Informationssicherheit festlegen können. KW - Informationssicherheitsmanagement KW - Cybersicherheit KW - Cybersecurity KW - Informationssicherheit KW - IT-Sicherheit Y1 - 2022 SN - 978-3-662-63757-9 SN - 978-3-662-63758-6 U6 - https://doi.org/10.1007/978-3-662-63758-6_15 SP - 373 EP - 398 PB - Springer Vieweg CY - Berlin ER - TY - CHAP A1 - Schuba, Marko A1 - Höfken, Hans-Wilhelm A1 - Linzbach, Sophie T1 - An ICS Honeynet for Detecting and Analyzing Cyberattacks in Industrial Plants T2 - 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) N2 - 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 KW - Conpot KW - honeypot KW - honeynet KW - ICS KW - cybersecurity Y1 - 2022 SN - 978-1-6654-4231-2 SN - 978-1-6654-4232-9 U6 - https://doi.org/10.1109/ICECET52533.2021.9698746 N1 - 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET). 09-10 December 2021. Cape Town, South Africa. PB - IEEE ER - TY - CHAP A1 - Christian, Esser A1 - Montag, Tim A1 - Schuba, Marko A1 - Allhof, Manuel T1 - Future critical infrastructure and security - cyberattacks on charging stations T2 - 31st International Electric Vehicle Symposium & Exhibition and International Electric Vehicle Technology Conference (EVS31 & EVTeC 2018) Y1 - 2018 SN - 978-1-5108-9157-9 SP - 665 EP - 671 PB - Society of Automotive Engineers of Japan (JSAE) CY - Tokyo ER - TY - CHAP A1 - Galdi, Chiara A1 - Hartung, Frank A1 - Dugelay, Jean-Luc T1 - Videos versus still images: Asymmetric sensor pattern noise comparison on mobile phones T2 - Electronic Imaging N2 - 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. KW - Image Forensics KW - Mobile Phones KW - Image Database Y1 - 2017 U6 - https://doi.org/10.2352/ISSN.2470-1173.2017.7.MWSF-331 SN - 2470-1173 N1 - IS&T International Symposium on Electronic Imaging 2017 Media Watermarking, Security, and Forensics 2017 SP - 100 EP - 103 PB - Society for Imaging Science and Technology CY - Springfield, Virginia ER -