Threshold signatures have improved the scalability of BFT protocols by replacing all-to-all broadcast with star-based communication. On the flip side, this approach renders network re-organization and performance optimization more costly, because information can only be exchanged between the leader and all other nodes.We present StarReact,an extension for BFT protocols that relies on star-based communication to collect and disseminate quorum certificates. This extension allows all nodes of the system to measure and evaluate the network state by monitoring the messages disseminated by the leader. Each node decides independently if performance degradation justifies a leader change. By deploying a median filter, the measured commit latency of the system is evaluated by each node to determine if a network change has a lasting effect that warrants a change in leadership or should be ignored. We showcase how StarReact is able to identify important network changes for different deployment scenarios and explain how view change executions should be adapted to different environments to improve commit latency and potentially throughput for such systems.

Measuring instruments are increasingly defined by complex software while using simple hardware sensors.For such systems, software conformity between certified prototypes and devices in the field is usually demonstrated using version numbers and hashes over executable code.Legal requirements for regulated instruments could equally be satisfied if prototype and device in the field display identical functional behavior even if hashes differ.Such functional identification can give instrument manufacturers room for software patches and bugfixes without the need for recertification.Based on the L * algorithm, which is used to learn the language which deterministic finite automata accept, a risk-based method is proposed that realizes automatic functional identification of software to a certain extent, thereby enabling quality control of regularly updated measuring instruments without the need for frequent manual inspections.Risk assessment may be used to identify critical state transitions in monitored devices, which can be used to trigger recertifications if needed.

Classic BFT protocols are often deployed in the LAN setting, with low delays and reliable links. Recent publications of BFT protocols have decreased drastically in their communication complexity and the application to previously unsuited areas such as mobile or sensor networks is getting more traction. To facilitate that development and showcase suitability under harsher network conditions, we take one of the most recent BFT protocols, HotStuff, and analyze the impact of increased and varying network delays on its performance. We apply the delay prediction scheme AWARE and make some simple modifications to the prediction algorithm in order to increase its performance even further.

The publication of the pivotal state machine replication protocol PBFT laid the foundation for a body of BFT protocols.We introduce a probabilistic model for evaluating BFT protocols in the presence of dynamic link and crash failures.The model is derived from the communication pattern, facilitating an adaptation to other protocols.The state of replicas is captured and used to derive the success probability of the protocol execution.To this end, we examine the influence of link and crash failure rates as well as the number of replicas.A comparison in protocol behavior of PBFT, Zyzzyva and SBFT is performed.

Since the introduction of the Measuring Instruments Directive 2014/32/EU, prototypes of measuring instruments subject to legal control in the European Union must be accompanied by a risk assessment, when being submitted for conformity assessment.Taximeters, water meters, electricity meters or fuel pumps form the basis for the economic sector usually known as Legal Metrology, where the development towards cheaper allpurpose hardware combined with more sophisticated software is imminent.Therefore, a risk assessment will always have to include software-related issues.Hitherto, publications about software risk assessment methods lack an efficient means to derive and assess suitable countermeasures for risk mitigation.To this end, attack trees are used in related research fields.In this paper, defense probability trees are derived from attack probability trees, well-suited to the requirements of software risk assessment and used to identify optimal sets of countermeasures.The infamous Meltdown vulnerability is used to highlight the experimental application of the method.

The Measuring Instruments Directive sets down essential requirements for measuring instruments subject to legal control in the EU.It dictates that a risk assessment must be performed before such instruments are put on the market.Because of the increasing importance of software in measuring instruments, a specifically tailored software risk assessment method has been previously developed and published.Related research has been done on graphical representation of threats by attack probability trees.The final stage is to formalize the method to prove its reproducibility and resilience against the complexity of future instruments.To this end, an inter-institutional comparison of the method is currently being conducted across national metrology institutes, while the weighing equipment manufacturers' association CECIP has provided a new measuring instrument concept, as a significant example of complex instruments.Based on the results of the comparison, a template to formalize the software risk assessment method is proposed here.This work was conducted within the frame of work package 4 of the European Metrology Cloud Project.

In the field of Legal Metrology, a risk assessment is demanded by European directives for certain measuring instruments.In this paper, a previously published reference cloud architecture will be subjected to such an assessment to demonstrate its suitability for providing adequate software protection.A specially tailored and standardized method is used to identify essential threats and common attack vectors for the reference architecture.With the help of calculated probability score and risk factors, the fulfillment of the essential requirements of the applicable European directives are shown.Furthermore, Attack Probability Trees are applied to more complex scenarios to identify suitable countermeasures to increase the resilience level where necessary.

Since software plays an ever more important role in measuring instruments, risk assessments for such instruments required by European regulations will usually include also a risk assessment of the software.Although previously introduced methods still lack efficient means for the representation of attacker motivation and have no prescribed way of constructing attack scenarios, attack trees have been used for several years in similar application scenarios.These trees are here developed into attack probability trees, specifically tailored to meet the requirements for software risk assessment.A real-world example based on taximeters is given to illustrate the application of attack probability trees approach and their advantages.

In Europe, measuring instruments subject to legal control are responsible for an annual turnover of 500 billion Euros and need to pass a conformity assessment with respect to European directives or national legislation before they can be used.Today, measuring instruments are frequently integrated into open networks and even branch into the areas of cloud computing and Internet of Things.Since software is one of the key components of such devices, Germany's national metrology institute, the Physikalisch-Technische Bundesantalt, is developing a method to assess the risks and evaluate current threats associated with software.The method uses the structure of and combines elements from the international ISO/IEC standards 27005 and 15408.It could be helpful for conformity assessment bodies and industry alike and supports the comparability of risk assessment results.Despite its focus on legal metrology, the method is applicable to other areas where software risk assessment is required, too.

Temporal in loop filters present one possible way to reduce noise introduced in compressed video sequences at low bit rates. Some of these filtering approaches make use of the quantized and generally noisy motion information conveyed in the bit stream generated by the encoder. One key feature of such filters is an adaptive filter length depending on the image content and the quality of the motion field. This paper derives mathematical equations to model the behaviour of one such filter in the presence of noisy motion vectors. The predicted optimal filter lengths are demonstrated to have a global optimum. They also show strong correlation with a real-world implementation of the previously introduced Temporal Trajectory Filter based on the HEVC main profile.

Summary form only given. Spatial in loop filters are a well established tool to improve the compression performance of today's video codecs. Temporal denoising and deblocking filters have recently also received some attention, because of their ability to stabilize pictures and to reduce flickering artifacts. One such filter, the previously introduced Quad tree-based Temporal Trajectory Filter, can produce good results, provided that the associated quad tree is sufficiently detailed. In this paper a novel, generally applicable scheme to compress such quad tree information is presented. In addition, the performance of the filter within the current HEVC test model HM 8.0 is investigated.

Block based motion compensated prediction still is the main technique used for temporal redundancy reduction in modern hybrid video codecs. However, the resulting motion vector fields are highly redundant as well. So, motion vector prediction and difference coding are used to compress such vector fields. A drawback of common motion vector prediction techniques is their inability to predict complex motion such as rotation and zoom in an efficient way. We present a novel Merge candidate for improving already existing vector prediction techniques based on higher order motion models to overcome this issue. To transmit the needed models, an efficient compression scheme is utilized. The improvement results in bit rate savings of 1.7% in average and up to 4% respectively.

Inloop filters are a well known tool to improve the compression performance of hybrid video codecs. These filters generally work in the spatial domain. If temporal filters are used instead, their performance strongly depends on the available motion data. In this paper a new method to retrieve accurate motion information per pixel is introduced and evaluated within the HEVC test model HM 8.0. The interpolated motion vector field is significnatly closer to the real object motion than the block-based motion data. An average bit rate reduction of 0.4% is observed with a maximum reduction of 2.0%.

Motion compensated inter prediction is a powerful tool used in modern hybrid video codecs to reduce the temporal redundancy of video sequences. However, the motion information needed for motion compensation is highly redundant as well. Thus, motion vector prediction and difference coding is a common method in modern video codecs. During the standardization of HEVC, new methods for motion prediction such as temporal motion vector prediction have been analyzed. This paper presents a method for motion vector prediction from perspective motion models in random access scenarios with hierarchical group of picture structures. To enable this kind of prediction a dynamic buffer system for generating, compressing and transmitting the underlying motion models is introduced. Bit rate reductions of up to 5% underline the performance of the complete system.

In both the HEVC draft and in H.264/AVC, in-loop filters are employed to improve the subjective and the objective quality of compressed video sequences. These filters use spatial information from a single frame only. Temporal Trajectory Filtering (TTF) constitutes an alternative approach which performs filtering in the temporal domain instead. In this work, a combination of the TTF with a quadtree partitioning algorithm for applying different filter parameters to different image regions is proposed and investigated. Experiments were conducted in the environment of the HEVC test model HM 3.0. Bit rate reductions of up to 9% for the low delay high efficiency setting of HEVC are reported.

In the context of the HEVC standardization activity, in-loop filters such as the adaptive loop filter and the deblocking filter are currently under investigation. Both filters work in the spatial domain only, despite the temporal correlation within video sequences. In this work a previously introduced filter, that uses temporal information for deblocking and denoising instead, is integrated into the HEVC test model HM 3.0. It is shown how the filter is to be adapted to work in combination with the adaptive loop filter for the HEVC low-delay profile. In addition, an optimal weighting function for the filtered luma samples based on the qunatization parameter is derived. Bit rate reductions of up to 7.6% are reported for individual sequences.

Most in-loop filters currently being employed in video compression algorithms use spatial information from a single frame of the video sequence only. In this paper, a new filter is introduced and investigated that combines both spatial and temporal information to provide subjective and objective quality improvement. The filter only requires a small overhead on slice level while using the temporal information conveyed in the bit stream to reconstruct the individual motion trajectory of every pixel in a frame at both encoder and decoder. This information is then used to perform pixel-wise adaptive motion-compensated temporal filtering. It is shown that the filter performs better than the state-of-the-art codec H.264/AVC over a large range of sequences and bit rates. Additionally, the filter is compared with another, Wiener-based in-loop filtering approach and a complexity analysis of both algorithms is conducted.

Coding artifacts in video codecs can be reduced using several spatial in-loop filters that are part of the emerging video coding standard High Efficiency Video Coding (HEVC). In this paper, we introduce the concept of global motion temporal filtering. A theoretical framework for a concept combining the temporal overlapping of several noisy versions of the same signal is introduced. This includes a model of the motion estimation error. As an important result, it is shown that an optimum number of framesNfor filtering exists. An implementation of the concept based on several versions of the HEVC test model using global motion-compensated temporal filtering shows that significant gains can be achieved.

In this work the application of a temporal in-loop filtering approach for B-frames in video compression based on the Temporal Trajectory Filter (TTF) is investigated. The TTF constructs temporal pixel trajectories for individual image points in the P-frames of a video sequence, which can be utilized to improve the quality of the reconstructed frames used for prediction. It is shown, how this concept can be adapted to B-frames despite the fact that these already use temporal motion information to a great extent through the flexible choice of reference frames and prediction modes. The proposed filter has been integrated into the H.264/AVC encoder using the extended profile with hierarchical B-frames and was tested on a wide range of sequences. The filter produces bit rate reductions of up to -4% with an average of -1.6% over all tested sequences while also improving the subjective quality of the decoded video.

The objective of this work is to investigate the performance of a new inloop filter for video compression, which uses temporal rather than spatial information to improve the quality of reference frames used for prediction. The new filter has been integrated into the H.264/AVC baseline encoder and tested on a wide range of sequences. Experimental results show that the filter achieves a bit rate reduction of up to 12% and more than 4% on average without increasing the complexity of either encoder or decoder significantly.