Author: Andreas Dittrich Ph.D. Thesis, submitted to the Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin, December 9, 2014. Oral defense March 20, 2015. Grade: magna cum laudae. Reviewers: Prof. Dr. Miroslaw Malek, Lugano, Switzerland Prof. Dr. Alexander Reinefeld, Berlin, Germany Prof. Dr. Jörg Kaiser, Magdeburg, Germany Download: published version, meta information

Dependability of service provision is one of the primary goals in modern networks. Since providers and clients are part of a connecting Information and Communications Technology (ICT) infrastructure, service dependability varies with the position of actors as the ICT devices needed for service provision change. We present two approaches to quantify user-perceived service dependability. The first is a model-driven approach to calculate instantaneous service availability. Using input models of the service, the infrastructure and a mapping between the two to describe actors of service communication, availability models are automatically created by a series of model to model transformations. The feasibility of the approach is demonstrated using exemplary services in the network of University of Lugano, Switzerland. The second approach aims at the responsiveness of the service discovery layer, the probability to find service instances within a deadline even in the presence of faults, and is the main part of this thesis. We present a hierarchy of stochastic models to calculate user-perceived responsiveness based on monitoring data from the routing layer. Extensive series of experiments have been run on the Distributed Embedded Systems (DES) wireless testbed at Freie Universität Berlin. They serve both to demonstrate the shortcomings of current discovery protocols in modern dynamic networks and to validate the presented stochastic models. Both approaches demonstrate that the dependability of service provision indeed differs considerably depending on the position of service clients and providers, even in highly reliable wired networks. The two approaches enable optimization of service networks with respect to known or predicted usage patterns. Furthermore, they anticipate novel service dependability models which combine service discovery, timeliness, placement and usage, areas that until now have been treated to a large extent separately.

	Authors: Andreas Dittrich Daniel Solis Herrera Pablo Coto Miroslaw Malek 20th IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2014, Singapore, November 18-21, 2014 Download: accepted version, final published version

Service Discovery (SD) is an integral part of service networks. Before a service can be used, it needs to be discovered successfully. Thus, a comprehensive service dependability analysis needs to consider the dependability of the SD process. As a time-critical operation, an important property of SD is responsiveness: the probability of successful discovery within a deadline, even in the presence of faults. This is especially true for dynamic networks with complex fault behavior such as wireless networks. We present results of a comprehensive responsiveness evaluation of decentralized SD, specifically active SD using the Zeroconf protocol. The ExCovery experiment framework has been employed in the Distributed Embedded System (DES) wireless testbed at Freie Universität Berlin. We present and discuss the experiment results and show how SD responsiveness is affected by the position and number of requesters and providers as well as the load in the network. Results clearly demonstrate that in all but the most favorable conditions, the configurations of current SD protocols struggle to achieve a high responsiveness. We further discuss results reflecting the long-term behavior of the testbed and how its varying reliability impacts SD responsiveness.

	Authors: Jelena Milosevic Andreas Dittrich Alberto Ferrante Miroslaw Malek Camilo Rojas Quiros Rubén Braojos Giovanni Ansaloni David Atienza 41st Computing in Cardiology Conference, CinC 2014, Cambridge, MA, USA, September 7-10, 2014 Download: author version, final published version

We present a methodology for identifying patients who have experienced Paroxysmal Atrial Fibrillation (PAF) among a given subjects population. Our work is intended as an initial step towards the design of an unobtrusive system for concurrent detection and monitoring of chronic cardiac conditions.

Our methodology comprises two stages: off-line training and on-line analysis. During training the most significant features are selected using machine-learning methods, without relying on a manual selection based on previous knowledge. Analysis is based on two phases: feature extraction and detection of PAF patients. Light-weight algorithms are employed in the feature extraction phase, allowing the on-line implementation of this step on wearable and resource-constrained sensor nodes. The detection phase employs techniques borrowed from the field of failure prediction. While these algorithms have found extensive applications in diverse scenarios, their application to automated cardiac analysis has not been sufficiently investigated.

Obtained results, in terms of performance, are comparable to similar efforts in the field. Nonetheless, the proposed method employs computationally simpler and more efficient algorithms, which are compatible with the computational constraints of state-of-the-art body sensor nodes.

	Authors: Jelena Milosevic Andreas Dittrich Miroslaw Malek Alberto Ferrante 3rd International Workshop on Security of Mobile Applications, IWSMA 2014, Fribourg, Switzerland, September 8-12, 2014 Download: accepted version, final published version

With explosive growth in the number of mobile devices, the mobile malware is rapidly spreading. Existing solutions, which are mainly based on binary signatures, are no longer effective, making security one of the key issues. The main contribution of this paper is a novel methodology to design and implement secure mobile devices by offering a resource-optimized method that combines efficient, light-weight malware detection on the device with high precision detection methods on cloud servers. We focus on early detection of behavioral patterns of malware families rather than the detection of malware binary signatures. Together with the alarm about the device being attacked, damage that detected type of malware can cause is estimated. Furthermore, the database with behavioral patterns is continuously updated, thus keeping a device resistant to new malware families.

	Authors: Andreas Dittrich Stefan Wanja Miroslaw Malek 28th IEEE International Symposium on Parallel Distributed Processing, Workshops and PhD Forum, IPDPSW 2014, Phoenix, AZ, USA, May 19-23, 2014 Download: accepted version, final published version GitHub Project Page and Source Repository

Experiments are a fundamental part of science. They are needed when the system under evaluation is too complex to be analytically described and they serve to empirically validate hypotheses. This work presents the experimentation framework ExCovery for dependability analysis of distributed processes. It provides concepts that cover the description, execution, measurement and storage of experiments. These concepts foster transparency and repeatability of experiments for further sharing and comparison. ExCovery has been tried and refined in a manifold of dependability related experiments during the last two years. A case study is provided to describe service discovery as experiment process. A working prototype for IP networks runs on the Distributed Embedded System (DES) wireless testbed at the Freie Universität Berlin.