TY - CHAP A1 - Walter, Peter A1 - Elsen, Ingo A1 - Müller, Holger A1 - Kraiss, Karl-Friedrich T1 - 3D object recognition with a specialized mixtures of experts architecture T2 - IJCNN'99. International Joint Conference on Neural Networks. Proceedings N2 - 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. Y1 - 1999 SN - 0-7803-5529-6 U6 - http://dx.doi.org/10.1109/IJCNN.1999.836243 SN - 1098-7576 N1 - Washington, DC 10-16.07.1999 SP - 3563 EP - 3568 PB - IEEE CY - New York ER - 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 - 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 - http://dx.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 - 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 - http://dx.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 - 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 - http://dx.doi.org/10.3139/9783446473478.012 SP - 193 EP - 218 PB - Carl Hanser Verlag CY - München 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 - http://dx.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 - Neth, Jannik A1 - Schuba, Marko A1 - Brodkorb, Karsten A1 - Neugebauer, Georg A1 - Höner, Tim A1 - Hack, Sacha T1 - Digital forensics triage app for android T2 - ARES '23: Proceedings of the 18th International Conference on Availability, Reliability and Security N2 - Digital forensics of smartphones is of utmost importance in many criminal cases. As modern smartphones store chats, photos, videos etc. that can be relevant for investigations and as they can have storage capacities of hundreds of gigabytes, they are a primary target for forensic investigators. However, it is exactly this large amount of data that is causing problems: extracting and examining the data from multiple phones seized in the context of a case is taking more and more time. This bears the risk of wasting a lot of time with irrelevant phones while there is not enough time left to analyze a phone which is worth examination. Forensic triage can help in this case: Such a triage is a preselection step based on a subset of data and is performed before fully extracting all the data from the smartphone. Triage can accelerate subsequent investigations and is especially useful in cases where time is essential. The aim of this paper is to determine which and how much data from an Android smartphone can be made directly accessible to the forensic investigator – without tedious investigations. For this purpose, an app has been developed that can be used with extremely limited storage of data in the handset and which outputs the extracted data immediately to the forensic workstation in a human- and machine-readable format. KW - Android KW - Digital triage KW - Triage-app Y1 - 2023 SN - 9798400707728 U6 - http://dx.doi.org/10.1145/3600160.3605017 N1 - ARES 2023: The 18th International Conference on Availability, Reliability and Security. August 29 - September 1, 2023. Benevento, Italy. PB - ACM ER - TY - CHAP A1 - Hebel, Christoph A1 - Herrmann, Ulf A1 - Ritz, Thomas A1 - Röth, Thilo A1 - Anthrakidis, Anette A1 - Böker, Jörg A1 - Franzke, Till A1 - Grodzki, Thomas A1 - Merkens, Torsten A1 - Schöttler, Mirjam T1 - FlexSHARE – Methodisches Framework zur innovativen Gestaltung der urbanen Mobilität durch Sharing- Angebote T2 - Transforming Mobility – What Next? N2 - Das Ziel des INTERREG-Projektes „SHAREuregio“ (FKZ: 34.EFRE-0300134) ist es, grenzüberschreitende Mobilität in der Euregio Rhein-Maas-Nord zu ermöglichen und zu fördern. Dazu soll ein elektromobiles Car- und Bikesharing- System entwickelt und in der Stadt Mönchengladbach, im Kreis Viersen sowie in den Gemeinden Roermond und Venlo (beide NL) zusammen mit den Partnern Wirtschaftsförderung Mönchengladbach, Wirtschaftsförderung für den Kreis Viersen, NEW AG, Goodmoovs (NL), Greenflux (NL) und der FH Aachen implementiert werden. Zunächst richtet sich das Angebot, bestehend aus 40 Elektroautos und 40 Elektrofahrrädern, an Unternehmen und wird nach einer Erprobungsphase, mit einer größeren Anzahl an Fahrzeugen, auch für Privatpersonen verfügbar gemacht werden. Die Fahrzeuge stehen bei den jeweiligen Anwendungspartnern in Deutschland und den Niederlanden. Im Rahmen dieses Projektes hat die FH Aachen „FlexSHARE“ entwickelt – ein methodisches Framework zur innovativen Gestaltung urbaner Sharing- Angebote. Das Framework ermöglicht es, anhand von messbaren Kenngrößen, bedarfsgerechte und auf die Region abgestimmte Sharing-Systeme zu entwickeln. Y1 - 2022 SN - 978-3-658-36429-8 U6 - http://dx.doi.org/10.1007/978-3-658-36430-4_10 N1 - Tagungsband zum 13. Wissenschaftsforum Mobilität, Beiträge des Wissenschaftsforums SP - 153 EP - 169 PB - Springer Gabler CY - Wiesbaden ER - TY - CHAP A1 - Grund, Raphael M. A1 - Altherr, Lena ED - Reiff-Stephan, Jörg ED - Jäkel, Jens ED - Schwarz, André T1 - Development of an open source energy disaggregation tool for the home automation platform Home Assistant T2 - Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel N2 - In order to reduce energy consumption of homes, it is important to make transparent which devices consume how much energy. However, power consumption is often only monitored aggregated at the house energy meter. Disaggregating this power consumption into the contributions of individual devices can be achieved using Machine Learning. Our work aims at making state of the art disaggregation algorithms accessibe for users of the open source home automation platform Home Assistant. KW - Home Automation Platform KW - Home Assistant KW - Open Source KW - Machine Learning KW - Energy Disaggregation Y1 - 2023 SN - 978-3-910103-01-6 U6 - http://dx.doi.org/10.33968/2023.02 N1 - 19. AALE-Konferenz. Luxemburg, 08.03.-10.03.2023. BTS Connected Buildings & Cities Luxemburg (Tagungsband unter https://doi.org/10.33968/2023.01) SP - 11 EP - 20 PB - le-tex publishing services GmbH CY - Leipzig 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 - http://dx.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 -