Inst. Softwaretechnik und Theoretische Informatik

271 Items

Recent Submissions
Exploring Deployment Strategies for the Tor Network

Döpmann, Christoph ; Rust, Sebastian ; Tschorsch, Florian (2018)

In response to upcoming performance and security challenges of anonymity networks like Tor, it will be of crucial importance to be able to develop and deploy performance improvements and state-of-the-art countermeasures. In this paper, we therefore explore different deployment strategies and review their applicability, impact, and risks to the Tor network. In a simulation-based evaluation, whic...

Towards a Concurrent and Distributed Route Selection for Payment Channel Networks

Rohrer, Elias ; Laß, Jann-Frederik ; Tschorsch, Florian (2017)

Payment channel networks use off-chain transactions to provide virtually arbitrary transaction rates. In this paper, we provide a new perspective on payment channels and consider them as a flow network. We propose an extended push-relabel algorithm to find payment flows in a payment channel network. Our algorithm enables a distributed and concurrent execution without violating capacity constrai...

Webchain: Verifiable Citations and References for the World Wide Web

Rohrer, Elias ; Heidel, Steffen ; Tschorsch, Florian (2018)

Readers’ capability to consider and assess sources is imperative. Digital preservation efforts, however, mostly neglected citation provenance, which is a necessity for transparent source verification. We therefore present Webchain, a new system enabling verifiable citations and references on the World Wide Web. Its architecture combines a distributed ledger with secure timestamping to ensure hi...

CircuitStart: A Slow Start For Multi-Hop Anonymity Systems

Döpmann, Christoph ; Tschorsch, Florian (2018)

In order to improve the performance of anonymity networks like Tor, custom transport protocols have been proposed to efficiently deal with the multi-hop nature of such overlay networks. In this work, we tackle the issue of quickly, but safely, ramping up the congestion window during the initial phase of a circuit's lifetime. We propose a tailored startup mechanism called CircuitStart that trans...

P2KMV: A Privacy-preserving Counting Sketch for Efficient and Accurate Set Intersection Cardinality Estimations

Sparka, Hagen ; Tschorsch, Florian ; Scheuermann, Björn (2018-05-01)

In this paper, we propose P2KMV, a novel privacy-preserving counting sketch, based on the k minimum values algorithm. With P2KMV, we offer a versatile privacy-enhanced technology for obtaining statistics, following the principle of data minimization, and aiming for the sweet spot between privacy, accuracy, and computational efficiency. As our main contribution, we develop methods to perform set...

Exploring Deployment Strategies for the Tor Network [Extended Version]

Döpmann, Christoph ; Rust, Sebastian ; Tschorsch, Florian (2018-07-07)

In response to upcoming performance and security challenges of anonymity networks like Tor, it will be of crucial importance to be able to develop and deploy performance improvements and state-of-the-art countermeasures. In this paper, we therefore explore different deployment strategies and review their applicability to the Tor network. In particular, we consider flag day, dual stack, translat...

Representations and optimizations for embedded parallel dataflow languages

Alexandrov, Alexander (2019)

Parallel dataflow engines such as Apache Hadoop, Apache Spark, and Apache Flink have emerged as an alternative to relational databases more suitable for the needs of modern data analysis applications. One of the main characteristics of these systems is their scalable programming model, based on distributed collections and parallel transformations. Notable examples are Flink’s DataSet and Spark’...

One-class classification in the presence of point, collective, and contextual anomalies

Görnitz, Nico (2019)

Anomaly detection has a prominent position in the processing pipeline of any real-world data-driven application. Its central goal is to detect and separate valid data points from malicious-anomalous-ones such that the cleaned data set can be processed further. In many applications, anomalies are even the prime objects of interest and need to be exposed early in order to avoid loss, e.g. in cred...

Brain-Computer Interface - Motor Imagery Data

Blankertz, Benjamin ; Vidaurre, Carmen ; Sannelli, Claudia ; Kübler, Andrea ; Halder, Sebastian ; Hammer, Eva-Maria (2019-01)

We provide a data set of a BCI study using a motor imagery paradigm. In a calibration session, participants were instructed by cues to perform different types of imagined movements. The pair of classes resulting in the most promising discrimination was chosen and a classifier was trained. That classifier was used in the feedback session to let the participants move a cursor horizontally accordi...

Opening the machine learning black box with Layer-wise Relevance Propagation

Lapuschkin, Sebastian (2019)

Machine learning techniques such as (Deep) Neural Networks are successfully solving a plethora of tasks, e.g. in image recognition and text analysis, and provide novel predictive models for complex physical, biological and chemical systems. However, due to the nested complex and non-linear structure of many machine learning models, this comes with the disadvantage of them acting as a black box,...

Computational modeling of glutamate-induced calcium signal generation and propagation in astrocytes

Oschmann, Franziska (2018)

Since the 1990s researchers have shown that astrocytes generate calcium oscillations in response to neuronal activity and propagate them as intercellular calcium waves over long distances. Moreover, astrocytes release transmitters in a calcium-dependent manner and by that signal to neurons. These discoveries have made astrocytes and especially calcium signal generation and propagation in astroc...

A statistical physics approach to inference problems on random networks

Bachschmid Romano, Ludovica (2018)

Recent advances in measurement technologies have resulted in the availability of large datasets from a variety of fields spanning the natural and social sciences. This posed the challenge to develop new statistical tools to extract relevant information from the data. A paradigmatic model that has been successfully applied to analyze large datasets is the Ising model of binary spins interacting ...

Investigating the effects of weak extracellular fields on single neurons: a modelling approach

Aspart, Florian (2018)

In the past decades, the rise of transcranial current stimulation (tCS) has sparkled an increasing interest in the effects of weak extracellular electric fields on neural activity. These fields, such as induced during tCS, have been shown to polarize the neuronal membrane and, consequently, to modulate the spiking activity. In this thesis, I follow a modelling approach to investigate how single...

Low dimensional visualization and modelling of data using distance-based models

Grünhage, Gina (2018)

This thesis consists of two parts, which seek low-dimensional representations for visualization and analysis of data. Both parts use rather different types of models and inference methods. In both cases, however, the models show inherent invariances, which need to be coped with during the optimization procedures. The first part addresses a fundamental problem in machine learning, namely, the ch...

Benchmarking dataflow systems for scalable machine learning

Boden, Christoph (2018)

The popularity of the world wide web and its ubiquitous global online services have led to unprecedented amounts of available data. Novel distributed data processing systems have been developed in order to scale out computations and analysis to such massive data set sizes. These "Big Data Analytics" systems are also popular choices to scale out the execution of machine learning algorithms. Howe...

Formal verification of model refactorings for hybrid control systems

Schlesinger, Sebastian (2018)

The ever growing complexity in modern embedded systems require to incorporate increasingly many functions into a single system. Such increasing functionality leads to growing design complexity. Model Driven Engineering (MDE) has been proposed to improve the complexity management for development of embedded systems. An industrially widely used technique to reduce the complexity of models and est...

Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

Laskov, Pavel ; Schäfer, Christin ; Kotenko, Igor ; Müller, Klaus-Robert (2004)

The anomaly detection methods are receiving growing attention in the intrusion detection community. The two main reasons for this are their ability to handle large volumes of unlabeled data and to detect previously unknown attacks. In this contribution we investigate the application of a modern machine learning technique – one-class Support Vector Machines (SVM) – for anomaly detection in unlab...

A Neural Network Model for the Self-Organization of Cortical Grating Cells

Bauer, Christoph ; Burger, Thomas ; Stetter, Martin ; Lang, Elmar W. (2000)

A neural network model with incremental Hebbian learning of afferent and lateral synaptic couplings is proposed,which simulates the activity-dependent self-organization of grating cells in upper layers of striate cortex. These cells, found in areas V1 and V2 of the visual cortex of monkeys, respond vigorously and exclusively to bar gratings of a preferred orientation and periodicity. Response b...

Multimodal instrumentation and methods for neurotechnology out of the lab

Lühmann, Alexander von (2018)

In neuroscience and related fields, progress in instrumentation, computational power, and signal processing methods continuously provide novel and increasingly powerful tools toward the investigation of brain activity in real-time and everyday environments. Research into real-life and application-oriented, non-invasive neurotechnology bears a number of multidisciplinary challenges which need to...

Compiler assisted vulnerability assessment

Shastry, Bhargava (2018)

With computer software pervading every aspect of our lives, vulnerabilities pose an active threat. Moreover, with shorter software development cycles and a security-as-an-afterthought mindset, vulnerabilities in shipped code are inevitable. Therefore, recognizing and fixing vulnerabilities has gained in importance. At the same time, there is a demand for methods to diagnose vulnerabilities with...