@misc{SianiparWillemsMeinel2019, author = {Sianipar, Johannes Harungguan and Willems, Christian and Meinel, Christoph}, title = {Virtual machine integrity verification in Crowd-Resourcing Virtual Laboratory}, series = {2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA)}, journal = {2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA)}, publisher = {IEEE}, address = {New York}, isbn = {978-1-5386-9133-5}, issn = {2163-2871}, doi = {10.1109/SOCA.2018.00032}, pages = {169 -- 176}, year = {2019}, abstract = {In cloud computing, users are able to use their own operating system (OS) image to run a virtual machine (VM) on a remote host. The virtual machine OS is started by the user using some interfaces provided by a cloud provider in public or private cloud. In peer to peer cloud, the VM is started by the host admin. After the VM is running, the user could get a remote access to the VM to install, configure, and run services. For the security reasons, the user needs to verify the integrity of the running VM, because a malicious host admin could modify the image or even replace the image with a similar image, to be able to get sensitive data from the VM. We propose an approach to verify the integrity of a running VM on a remote host, without using any specific hardware such as Trusted Platform Module (TPM). Our approach is implemented on a Linux platform where the kernel files (vmlinuz and initrd) could be replaced with new files, while the VM is running. kexec is used to reboot the VM with the new kernel files. The new kernel has secret codes that will be used to verify whether the VM was started using the new kernel files. The new kernel is used to further measuring the integrity of the running VM.}, language = {en} } @inproceedings{GruenerMuehleGayvoronskayaetal.2019, author = {Gr{\"u}ner, Andreas and M{\"u}hle, Alexander and Gayvoronskaya, Tatiana and Meinel, Christoph}, title = {A quantifiable trustmModel for Blockchain-based identity management}, series = {IEEE 2018 International Congress on Cybermatics / 2018 IEEE Conferences on Internet of Things, Green Computing and Communications, cyber, physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology}, booktitle = {IEEE 2018 International Congress on Cybermatics / 2018 IEEE Conferences on Internet of Things, Green Computing and Communications, cyber, physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology}, publisher = {IEEE}, address = {New York}, isbn = {978-1-5386-7975-3}, doi = {10.1109/Cybermatics_2018.2018.00250}, pages = {1475 -- 1482}, year = {2019}, language = {en} } @article{MeinelGayvoronskayaMuehle2019, author = {Meinel, Christoph and Gayvoronskaya, Tatiana and M{\"u}hle, Alexander}, title = {Die Zukunftspotenziale der Blockchain-Technologie}, series = {Die Zukunft der Medizin : disruptive Innovationen revolutionieren Medizin und Gesundheit}, journal = {Die Zukunft der Medizin : disruptive Innovationen revolutionieren Medizin und Gesundheit}, publisher = {Medizinisch Wissenschaftliche Verlagsgesellschaft}, address = {Berlin}, isbn = {978-3-95466-398-9}, pages = {259 -- 280}, year = {2019}, language = {de} } @misc{PodlesnyKayemMeinel2019, author = {Podlesny, Nikolai Jannik and Kayem, Anne V. D. M. and Meinel, Christoph}, title = {Attribute Compartmentation and Greedy UCC Discovery for High-Dimensional Data Anonymisation}, series = {Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy}, journal = {Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {978-1-4503-6099-9}, doi = {10.1145/3292006.3300019}, pages = {109 -- 119}, year = {2019}, abstract = {High-dimensional data is particularly useful for data analytics research. In the healthcare domain, for instance, high-dimensional data analytics has been used successfully for drug discovery. Yet, in order to adhere to privacy legislation, data analytics service providers must guarantee anonymity for data owners. In the context of high-dimensional data, ensuring privacy is challenging because increased data dimensionality must be matched by an exponential growth in the size of the data to avoid sparse datasets. Syntactically, anonymising sparse datasets with methods that rely of statistical significance, makes obtaining sound and reliable results, a challenge. As such, strong privacy is only achievable at the cost of high information loss, rendering the data unusable for data analytics. In this paper, we make two contributions to addressing this problem from both the privacy and information loss perspectives. First, we show that by identifying dependencies between attribute subsets we can eliminate privacy violating attributes from the anonymised dataset. Second, to minimise information loss, we employ a greedy search algorithm to determine and eliminate maximal partial unique attribute combinations. Thus, one only needs to find the minimal set of identifying attributes to prevent re-identification. Experiments on a health cloud based on the SAP HANA platform using a semi-synthetic medical history dataset comprised of 109 attributes, demonstrate the effectiveness of our approach.}, language = {en} } @misc{SukmanaTorkuraGraupneretal.2019, author = {Sukmana, Muhammad Ihsan Haikal and Torkura, Kennedy A. and Graupner, Hendrik and Cheng, Feng and Meinel, Christoph}, title = {Unified Cloud Access Control Model for Cloud Storage Broker}, series = {33rd International Conference on Information Networking (ICOIN 2019)}, journal = {33rd International Conference on Information Networking (ICOIN 2019)}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-5386-8350-7}, issn = {1976-7684}, doi = {10.1109/ICOIN.2019.8717982}, pages = {60 -- 65}, year = {2019}, abstract = {Cloud Storage Broker (CSB) provides value-added cloud storage service for enterprise usage by leveraging multi-cloud storage architecture. However, it raises several challenges for managing resources and its access control in multiple Cloud Service Providers (CSPs) for authorized CSB stakeholders. In this paper we propose unified cloud access control model that provides the abstraction of CSP's services for centralized and automated cloud resource and access control management in multiple CSPs. Our proposal offers role-based access control for CSB stakeholders to access cloud resources by assigning necessary privileges and access control list for cloud resources and CSB stakeholders, respectively, following privilege separation concept and least privilege principle. We implement our unified model in a CSB system called CloudRAID for Business (CfB) with the evaluation result shows it provides system-and-cloud level security service for cfB and centralized resource and access control management in multiple CSPs.}, language = {en} } @misc{BartzYangBethgeetal.2019, author = {Bartz, Christian and Yang, Haojin and Bethge, Joseph and Meinel, Christoph}, title = {LoANs}, series = {Computer Vision - ACCV 2018 Workshops}, volume = {11367}, journal = {Computer Vision - ACCV 2018 Workshops}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-21074-8}, issn = {0302-9743}, doi = {10.1007/978-3-030-21074-8_29}, pages = {341 -- 356}, year = {2019}, abstract = {Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availability of large scale, fully annotated datasets, but the creation of such a dataset is a complicated and costly task. In this paper, we propose a novel method for weakly supervised object detection that simplifies the process of gathering data for training an object detector. We train an ensemble of two models that work together in a student-teacher fashion. Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student. The student uses this feedback to learn how to localize objects and is thus entirely supervised by the teacher, as we are using no labels for training the localizer. In our experiments, we show that our model is very robust to noise and reaches competitive performance compared to a state-of-the-art fully supervised approach. We also show the simplicity of creating a new dataset, based on a few videos (e.g. downloaded from YouTube) and artificially generated data.}, language = {en} } @misc{SeidelKrentzMeinel2019, author = {Seidel, Felix and Krentz, Konrad-Felix and Meinel, Christoph}, title = {Deep En-Route Filtering of Constrained Application Protocol (CoAP) Messages on 6LoWPAN Border Routers}, series = {2019 IEEE 5th World Forum on Internet of Things (WF-IoT)}, journal = {2019 IEEE 5th World Forum on Internet of Things (WF-IoT)}, publisher = {Institute of Electrical and Electronics Engineers}, address = {New York}, isbn = {978-1-5386-4980-0}, doi = {10.1109/WF-IoT.2019.8767262}, pages = {201 -- 206}, year = {2019}, abstract = {Devices on the Internet of Things (IoT) are usually battery-powered and have limited resources. Hence, energy-efficient and lightweight protocols were designed for IoT devices, such as the popular Constrained Application Protocol (CoAP). Yet, CoAP itself does not include any defenses against denial-of-sleep attacks, which are attacks that aim at depriving victim devices of entering low-power sleep modes. For example, a denial-of-sleep attack against an IoT device that runs a CoAP server is to send plenty of CoAP messages to it, thereby forcing the IoT device to expend energy for receiving and processing these CoAP messages. All current security solutions for CoAP, namely Datagram Transport Layer Security (DTLS), IPsec, and OSCORE, fail to prevent such attacks. To fill this gap, Seitz et al. proposed a method for filtering out inauthentic and replayed CoAP messages "en-route" on 6LoWPAN border routers. In this paper, we expand on Seitz et al.'s proposal in two ways. First, we revise Seitz et al.'s software architecture so that 6LoWPAN border routers can not only check the authenticity and freshness of CoAP messages, but can also perform a wide range of further checks. Second, we propose a couple of such further checks, which, as compared to Seitz et al.'s original checks, more reliably protect IoT devices that run CoAP servers from remote denial-of-sleep attacks, as well as from remote exploits. We prototyped our solution and successfully tested its compatibility with Contiki-NG's CoAP implementation.}, language = {en} } @incollection{BauerMalchowMeinel2019, author = {Bauer, Matthias and Malchow, Martin and Meinel, Christoph}, title = {Full Lecture Recording Watching Behavior, or Why Students Watch 90-Min Lectures in 5 Min}, series = {IMCL 2018: Mobile Technologies and Applications for the Internet of Things}, volume = {909}, booktitle = {IMCL 2018: Mobile Technologies and Applications for the Internet of Things}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-11434-3}, issn = {2194-5357}, doi = {10.1007/978-3-030-11434-3_38}, pages = {347 -- 358}, year = {2019}, abstract = {Many universities record the lectures being held in their facilities to preserve knowledge and to make it available to their students and, at least for some universities and classes, to the broad public. The way with the least effort is to record the whole lecture, which in our case usually is 90 min long. This saves the labor and time of cutting and rearranging lectures scenes to provide short learning videos as known from Massive Open Online Courses (MOOCs), etc. Many lecturers fear that recording their lectures and providing them via an online platform might lead to less participation in the actual lecture. Also, many teachers fear that the lecture recordings are not used with the same focus and dedication as lectures in a lecture hall. In this work, we show that in our experience, full lectures have an average watching duration of just a few minutes and explain the reasons for that and why, in most cases, teachers do not have to worry about that.}, language = {en} }