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Corporate Social Responsibility and Diversity Management. Theoretical Approaches and Best Practices
(2017)
This book provides unique insights into how the idea of quota laws to get women on to corporate boards gained international momentum from its origins in Norway. Invaluable insights are gained through the stories of actors involved in shaping the discourse and practice on women of boards.
In exploring political contexts, the role of the advocacy movement, experiences of women directors themselves and latest research findings, the contributors provide a comprehensive overview of the rationales, processes and outcomes of formal approaches to gender diversity on boards. Drawing on insights from political, business and academic actors, the book discusses how and why the Norwegian law on gender equality on corporate boards is turning into a blueprint for action internationally.
Getting Women on to Corporate Boards will prove an invaluable resource for policy-makers, principle-setters, practitioners and students interested in the international lessons from Norway, as well as for current and potential female directors.
Segmentation of radio-angiographic images using morphological filters, thinning and region growing
(1997)
Opportunities and Challenges in Mixed-Reality for an Inclusive Human-Robot Collaboration Environment
(2018)
This paper presents an approach to enhance robot control using Mixed-Reality. It highlights the opportunities and challenges in the interaction design to achieve a Human-Robot Collaborative environment. In fact, Human-Robot Collaboration is the perfect space for social inclusion. It enables people, who suffer severe physical impairments, to interact with the environment by providing them movement control of an external robotic arm. Now, when discussing about robot control it is important to reduce the visual-split that different input and output modalities carry. Therefore, Mixed-Reality is of particular interest when trying to ease communication between humans and robotic systems.
Design and Development of a Bioreactor System for Mechanical Stimulation of Musculoskeletal Tissue
(2023)
We report on the development of a bioreactor system for mechanical stimulation of musculoskeletal tissues. The ultimate object is to improve the quality of medical treatment following injuries of the enthesis tissue. To this end, the tissue formation process through the effect of mechanical stimulation is investigated. A six-well system was designed, 3D printed and tested. An integrated actuator creates strain by applying a force. A contactless position sensor monitors the travels. An electronic circuit controls the bioreactor using a microcontroller. An IoT platform connects the microcontroller to a smartphone, enabling the user to alter variables, trigger actions and monitor the system. The system was stabilised by implementing two PID controllers and safety measures. The results show that the bioreactor design is suited to execute mechanical stimulation and to investigate the tissue formation and regeneration process …
This Article introduces two research projects towards assistive robotic arms for people with severe body impairments. Both projects aim to develop new control and interaction designs to promote accessibility and a better performance for people with functional losses in all four extremities, e.g. due to quadriplegic or multiple sclerosis. The project MobILe concentrates on using a robotic arm as drinking aid and controlling it with smart glasses, eye-tracking and augmented reality. A user oriented development process with participatory methods were pursued which brought new knowledge about the life and care situation of the future target group and the requirements a robotic drinking aid needs to meet. As a consequence the new project DoF-Adaptiv follows an even more participatory approach, including the future target group, their family and professional caregivers from the beginning into decision making and development processes within the project. DoF-Adaptiv aims to simplify the control modalities of assistive robotic arms to enhance the usability of the robotic arm for activities of daily living. lo decide on exemplary activities, like eating or open a door, the future target group, their family and professional caregivers are included in the decision making process. Furthermore all relevant stakeholders will be included in the investigation of ethical, legal and social implications as well as the identification of potential risks. This article will show the importance of the participatory design for the development and research process in MobILe and DoF-Adaptiv.
We investigate the possibility to use update propagation methods for optimizing the evaluation of continuous queries. Update propagation allows for the efficient determination of induced changes to derived relations resulting from an explicitly performed base table update. In order to simplify the computation process, we propose the propagation of updates with different degrees of granularity which corresponds to an incremental query evaluation with different levels of accuracy. We show how propagation rules for diferent update granularities can be systematically derived, combined and further optimized by using Magic Sets. This way, the costly evaluation of certain subqueries within a continuous query can be systematically circumvented allowing for cutting down on the number of pipelined tuples considerably.
Cardiac and liver computed tomography (CT) perfusion has not been routinely implemented in the clinic and requires high radiation doses. The purpose of this study is to examine the radiation exposure and technical settings for cardiac and liver CT perfusion scans at different CT scanners. Two cardiac and three liver CT perfusion protocols were examined with the N1 LUNGMAN phantom at three multi-slice CT scanners: a single-source (I) and second- (II) and third-generation (III) dual-source CT scanners. Radiation doses were reported for the CT dose index (CTDIvol) and dose–length product (DLP) and a standardised DLP (DLP10cm) for cardiac and liver perfusion. The effective dose (ED10cm) for a standardised scan length of 10 cm was estimated using conversion factors based on the International Commission on Radiological Protection (ICRP) 110 phantoms and tissue-weighting factors from ICRP 103. The proposed total lifetime attributable risk of developing cancer was determined as a function of organ, age and sex for adults. Radiation exposure for CTDIvol, DLP/DLP10 cm and ED10 cm during CT perfusion was distributed as follows: for cardiac perfusion (II) 144 mGy, 1036 mGy·cm/1440 mGy·cm and 39 mSv, and (III) 28 mGy, 295 mGy·cm/279 mGy·cm and 8 mSv; for liver perfusion (I) 225 mGy, 3360 mGy·cm/2249 mGy·cm and 54 mSv, (II) 94 mGy, 1451 mGy·cm/937 mGy·cm and 22 mSv, and (III) 74 mGy, 1096 mGy·cm/739 mGy·cm and 18 mSv. The third-generation dual-source CT scanner applied the lowest doses. Proposed total lifetime attributable risk increased with decreasing age. Even though CT perfusion is a high-dose examination, we observed that new-generation CT scanners could achieve lower doses. There is a strong impact of organ, age and sex on lifetime attributable risk. Further investigations of the feasibility of these perfusion scans are required for clinical implementation.
The aim of this phantom study is to examine radiation doses of dual- and single-energy computed tomography (DECT and SECT) in the chest and upper abdomen for three different multi-slice CT scanners. A total of 34 CT protocols were examined with the phantom N1 LUNGMAN. Four different CT examination types of different anatomic regions were performed both in single- and dual-energy technique: chest, aorta, pulmonary arteries for suspected pulmonary embolism and liver. Radiation doses were examined for the CT dose index CTDIvol and dose-length product (DLP). Radiation doses of DECT were significantly higher than doses for SECT. In terms of CTDIvol, radiation doses were 1.1–3.2 times higher, and in terms of DLP, these were 1.1–3.8 times higher for DECT compared with SECT. The third-generation dual-source CT applied the lowest dose in 7 of 15 different examination types of different anatomic regions.
Three-dimensional magnetic resonance medical images may contain scanner- and patient-induced geometric distortion. For qualitative diagnosis, geometric errors of a few millimeters are often tolerated. However, quantitative applications such as image-guided neurosurgery and radiotherapy can require an accuracy of a millimeter or better. We have developed a method to accurately measure scanner-induced geometric distortion and to correct the MR images for this type of distortion. The method involves a number of steps. First, a specially designed phantom is scanned that contains a large number of reference structures on positions with a manufacturing error of less than 0.05 mm. Next, the positions of the reference structures are automatically detected in the scanned images and a higher-order polynomial distortion-correction transformation is estimated. Then the patient is scanned and the transformation is applied to correct the patient images for the detected distortion. The distortion-correction method is explained in detail in this paper. The accuracy of the method has been measured with synthetically generated phantom scans that contain an exactly-known amount and type of distortion. The reproducibility of the method has been measured by applying it to a series of consecutive phantom scans. Validation results are briefly described in this paper, a more-detailed description is given in another submission to SPIE Medical Imaging 2001.
Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.
The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems.
The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems.
The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available. Here we present DECIMER — Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.
The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available. Here we present DECIMER - Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.
Due to high power density and superior efficiency, polymer electrolyte membrane fuel cells (PEMFC) are believed to play a significant role for carbon dioxide emissions free electrical energy systems in the future. Unlike in Carnot processes, chemical energy in the form of hydrogen and oxygen is converted directly into electrical energy without a further process step. One issue in the development of PEMFCs for mobile or stationary applications is the utilization of rare and expensive catalyst material like platinum within the membrane electrode assembly (MEA) see figure 1. In addition, the objective is to reduce production costs and to increase the lifetime of PEMFC. One approach to improve PEMFCs is the development of intelligent electrode architectures. However, cost effective high performance materials are necessary to reach the development targets.
Membrane electrode assemblies (MEA) developed at the Westphalian Energy Institute for polymer electrolyte membrane fuel cells (PEMFC) are high tech systems containing various materials structured in nanoscale, at which electrochemical reactions occur on catalyst nano particle surfaces. For low reactance homogeneous compression of the MEA’s layers is necessary. A novel stack architecture for electrochemical cells, especially PEMFC as well as PEM electrolysers, has been developed according to achieve ideal cell operation conditions. Single cells of such a stack are inserted into flexible slots that are surrounded by hydraulic media. While operation the hydraulic media is pressurised which leads to an even compression and cooling of the stack’s cells. With this stack design it has been possible to construct a test facility for simultaneous characterisation of several MEA samples. As compression and temperature conditions of every single sample are equal, with the novel test system the effect of e.g. different electrode configurations can be investigated. Furthermore, the modular stack design leads to the development of hybrid energy applications combining fuel cells, electrolysers, batteries as well as metal hydride tanks in one system.
Leadership Beyond Narcissism: On the Role of Compassionate Love as Antecedent of Servant Leadership
(2020)
While we already know a lot about the outcomes and boundary conditions of servant leadership, there is still a need for research on its antecedents. Building on the theory of purposeful work behavior and further theorizing by van Dierendonck and Patterson (2015), we examine if leaders’ propensity for compassionate love will evoke servant leadership behavior. At the same time, we contrast compassionate love to leaders’ narcissism as psychological counterpart to compassionate love, because narcissism is not associated with leader effectiveness, but with leader emergence instead. We collected data from 170 leader-follower-dyads in a field study in Germany, while measuring leaders’ compassionate love and narcissism, and followers’ perceptions of servant leadership. We found a positive association between leaders’ compassionate love and servant leadership behavior, while narcissism was negatively associated with servant leadership. Theoretical and practical implications, as well as pathways for future research are discussed.
The purpose of the paper is to contribute to the inner workings of transformational leadership in the context of organizational change. According to the organizational role theory, role conflict is proposed as a mediator between transformational leadership and affective commitment to change and irritation. Cross-sectional data were collected in a German company in the textiles sector, undergoing a pervasive IT-related change. Confirmatory factor analysis and structural equation modeling was performed for validity and hypothesis testing. The findings suggest that role conflict acts as a full mediator in the relationship between transformational leadership and affective commitment to change, as well as irritation. Transformational leadership is often discussed in terms of change-oriented leadership. Surprisingly, only a few studies have examined the specific impact of transformational leadership on attitudinal outcomes during change processes, yet. Consequently, research on the underlying psychological mechanisms of the relationship is scarce, too.
The one-phonon inelastic low energy helium atom scattering theory is adapted to cases where the target monolayer is a p(1x1) commensurate square lattice. Experimental data for para-H2/NaCl(001) are re-analyzed and the relative intensities of energy loss peaks in the range 6 to 9 meV are determined. The case of the H2/NaCl(001) monolayer for 26 meV scattering energy is computationally challenging and difficult because it has a much more corrugated surface than those in the previous applications for triangular lattices. This requires a large number of coupled channels for convergence in the wave-packet-scattering calculation and a long series of Fourier amplitudes to represent the helium-target potential energy surface. A modified series is constructed in which a truncated Fourier expansion of the potential is constrained to give the exact value of the potential at some key points and which mimics the potential with fewer Fourier amplitudes. The shear horizontal phonon mode is again accessed by the helium scattering for small misalignment of the scattering plane relative to symmetry axes of the monolayer. For 1° misalignment, the calculated intensity of the longitudinal acoustic phonon mode frequently is higher than that of the shear horizontal phonon mode in contrast to what was found at scattering energies near 10 meV for triangular lattices of Ar, Kr, and Xe on Pt(111).
We present a scheme for cooling a vibrational mode of a magnetic molecular nanojunction by a spin-polarized charge current upon exploiting the interaction between its magnetic moment and the vibration. The spin-polarized charge current polarizes the magnetic moment of the nanoisland, thereby lowering its energy. A small but finite coupling between the vibration and the magnetic moment permits a direct exchange of energy such that vibrational energy can be transferred into the magnetic state. For positive bias voltages, this generates an effective cooling of the molecular vibrational mode. We determine parameter regimes for the cooling of the vibration to be optimal. Although the flowing charge current inevitably heats up the vibrational mode via Ohmic energy losses, we show that due to the magnetomechanical coupling, the vibrational energy (i.e, the effective phonon temperature) can be lowered below 50% of its initial value, when the two leads are polarized anti-parallel. In contrast to the cooling effect for positive bias voltages, net heating of the vibrational mode occurs for negative bias voltages. The cooling effect is enhanced for a stronger anti-parallel magnetic polarization of the leads, while the heating is stronger for a larger parallel polarization. Yet, dynamical cooling is also possible with parallel lead alignments when the two tunneling barriers are asymmetric.
Geometries, stabilities, electronic properties and NMR-shielding of cucurbit[6]uril–spermine host-ligand complexes are investigated with DFT calculations and compared to experimental results. Cucurbit[6]uril and spermine can form complexes with two different minimum energy geometries and corresponding characteristic differences in NMR shielding. The energetically preferred complex geometry has a perfect inversion symmetry and its proton NMR shielding agrees very well with experimental results. The cucurbit[6]uril host molecule shows a distinct geometrical flexibility in ligand binding which allows an induced fit of the spermine ligand. The energetic barrier for the rotation of spermine in the favourable complex is approximated to be in the order of a few kilocalories per mole.
Developing and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule fragmentation) is an iterative process. It involves repeated sequences of implementing a rule set, applying it to relevant structural data, checking the results, and adjusting the rules. This requires a computational workflow with data import, fragmentation algorithm integration, and result visualisation. The described workflow is normally unavailable for a new algorithm and must be set up individually. This work presents an open Java rich client Graphical User Interface (GUI) application to support the development of new in silico molecule fragmentation algorithms and make them readily available upon release. The MORTAR (MOlecule fRagmenTAtion fRamework) application visualises fragmentation results of a set of molecules in various ways and provides basic analysis features. Fragmentation algorithms can be integrated and developed within MORTAR by using a specific wrapper class. In addition, fragmentation pipelines with any combination of the available fragmentation methods can be executed. Upon release, three fragmentation algorithms are already integrated: ErtlFunctionalGroupsFinder, Sugar Removal Utility, and Scaffold Generator. These algorithms, as well as all cheminformatics functionalities in MORTAR, are implemented based on the Chemistry Development Kit (CDK).
Different charge treatment approaches are examined for cyclotide-induced plasma membrane disruption by lipid extraction studied with dissipative particle dynamics. A pure Coulomb approach with truncated forces tuned to avoid individual strong ion pairing still reveals hidden statistical pairing effects that may lead to artificial membrane stabilization or distortion of cyclotide activity depending on the cyclotide’s charge state. While qualitative behavior is not affected in an apparent manner, more sensitive quantitative evaluations can be systematically biased. The findings suggest a charge smearing of point charges by an adequate charge distribution. For large mesoscopic simulation boxes, approximations for the Ewald sum to account for mirror charges due to periodic boundary conditions are of negligible influence.
The influence of molecular fragmentation and parameter settings on a mesoscopic dissipative particle dynamics (DPD) simulation of lamellar bilayer formation for a C10E4/water mixture is studied. A “bottom-up” decomposition of C10E4 into the smallest fragment molecules (particles) that satisfy chemical intuition leads to convincing simulation results which agree with experimental findings for bilayer formation and thickness. For integration of the equations of motion Shardlow’s S1 scheme proves to be a favorable choice with best overall performance. Increasing the integration time steps above the common setting of 0.04 DPD units leads to increasingly unphysical temperature drifts, but also to increasingly rapid formation of bilayer superstructures without significantly distorted particle distributions up to an integration time step of 0.12. A scaling of the mutual particle–particle repulsions that guide the dynamics has negligible influence within a considerable range of values but exhibits apparent lower thresholds beyond which a simulation fails. Repulsion parameter scaling and molecular particle decomposition show a mutual dependence. For mapping of concentrations to molecule numbers in the simulation box particle volume scaling should be taken into account. A repulsion parameter morphing investigation suggests to not overstretch repulsion parameter accuracy considerations.
Web Service Security - XKMS
(2004)
Optimization of the laser remelting process for HVOF-sprayed Stellite 6 wear resistant coatings
(2016)
Cobalt base alloys are used in all industrial areas due to their excellent wear resistance. Several studies have shown that Stellite 6 coatings are suitable not only for protection against sliding wear, but also in case of exposure to impact loading. In this respect, a possible application is the protection of hydropower plant components affected by cavitation. The main problem in connection with Stellite 6 is the deposition procedure of the protective layers, both welding and thermal spraying techniques requesting special measures in order to prevent the brittleness of the coating. In this study, Stellite 6 layers were HVOF thermally sprayed on a martensitic 13-4 stainless steel substrate, as usually used for hydraulic machinery components. In order to improve the microstructure of the HVOF-sprayed coatings and their adhesion to the substrate, laser remelting was applied, using a TRUMPF Laser type HL 124P LCU and different working parameters. The microstructure of the coatings, obtained for various remelting conditions, was evaluated by light microscopy, showing the optimal value of the pulse power, which provided a homogenous Stellite 6 layer with good adhesion to the substrate.
From brain drain to brain exchange: how to use better highly skilled workers; a conceptual approach.
Flying insects employ elegant optical-flow-based strategies to solve complex tasks such as landing or obstacle avoidance. Roboticists have mimicked these strategies on flying robots with only limited success, because optical flow (1) cannot disentangle distance from velocity and (2) is less informative in the highly important flight direction. Here, we propose a solution to these fundamental shortcomings by having robots learn to estimate distances to objects by their visual appearance. The learning process obtains supervised targets from a stability-based distance estimation approach. We have successfully implemented the process on a small flying robot. For the task of landing, it results in faster, smooth landings. For the task of obstacle avoidance, it results in higher success rates at higher flight speeds. Our results yield improved robotic visual navigation capabilities and lead to a novel hypothesis on insect intelligence: behaviours that were described as optical-flow-based and hardwired actually benefit from learning processes.
Measurement studies are essential for research and industry alike to understand the Web’s inner workings better and help quantify specific phenomena. Performing such studies is demanding due to the dynamic nature and size of the Web. An experiment’s careful design and setup are complex, and many factors might affect the results. However, while several works have independently observed differences in
the outcome of an experiment (e.g., the number of observed trackers) based on the measurement setup, it is unclear what causes such deviations. This work investigates the reasons for these differences by visiting 1.7M webpages with five different measurement setups. Based on this, we build ‘dependency trees’ for each page and cross-compare the nodes in the trees. The results show that the measured trees differ considerably, that the cause of differences can be attributed to specific nodes, and that even identical measurement setups can produce different results.
Cookie notices (or cookie banners) are a popular mechanism for websites to provide (European) Internet users a tool to choose which cookies the site may set. Banner implementations range from merely providing information that a site uses cookies over offering the choice to accepting or denying all cookies to allowing fine-grained control of cookie usage. Users frequently get annoyed by the banner’s pervasiveness as they interrupt “natural” browsing on the Web. As a remedy, different browser extensions have been developed to automate the interaction with cookie banners.
In this work, we perform a large-scale measurement study comparing the effectiveness of extensions for “cookie banner interaction.” We configured the extensions to express different privacy choices (e.g., accepting all cookies, accepting functional cookies, or rejecting all cookies) to understand their capabilities to execute a user’s preferences. The results show statistically significant differences in which cookies are set, how many of them are set, and which types are set—even for extensions that aim to implement the same cookie choice. Extensions for “cookie banner interaction” can effectively reduce the number of set cookies compared to no interaction with the banners. However, all extensions increase the
tracking requests significantly except when rejecting all cookies.
Third-party tracking is a common and broadly used technique on the Web. Different defense mechanisms have emerged to counter these practices (e.g. browser vendors that ban all third-party cookies). However, these countermeasures only target third-party trackers and ignore the first party because the narrative is that such monitoring is mostly used to improve the utilized service (e.g. analytical services). In this paper, we present a large-scale measurement study that analyzes tracking performed by the first party but utilized by a third party to circumvent standard tracking preventing techniques. We visit the top 15,000 websites to analyze first-party cookies used to track users and a technique called “DNS CNAME cloaking”, which can be used by a third party to place first-party cookies. Using this data, we show that 76% of sites effectively utilize such tracking techniques. In a long-running analysis, we show that the usage of such cookies increased by more than 50% over 2021.
Software updates take an essential role in keeping IT environments secure. If service providers delay or do not install updates, it can cause unwanted security implications for their environments. This paper conducts a large-scale measurement study of the update behavior of websites and their utilized software stacks. Across 18 months, we analyze over 5.6M websites and 246 distinct client- and server-side software distributions. We found that almost all analyzed sites use outdated software. To understand the possible security implications of outdated software, we analyze the potential vulnerabilities that affect the utilized software. We show that software components are getting older and more vulnerable because they are not updated. We find that 95 % of the analyzed websites use at least one product for which a vulnerability existed.
Steganography, the art of concealing information in different types of medias, is a very old practice. Yet, it only recently started being used by malware operators on a large scale. Malware programmers and operators are increasing their efforts in developing covert communication channels between infected computers and their command and control servers. In addition to steganography, recent examples include hiding communication in inconspicuous network traffic such as DNS queries or HTTP 404 error messages.
When used properly, these covert communication channels can bypass many automated detection mechanisms and render malware communication difficult to detect and block. From an attacker's perspective, covert communication channels are a valuable addition because they allow messages to blend in with legitimate traffic and thus significantly lower the chance of being detected even when inspected by a human analyst.
This presentation studies recent advances in covert communication channels used by real-world malware. First, we will show how steganography has recently been used in three different malware families (Stegoloader, Vawtrak, and Lurk). We will dive into the implementation details on how steganography is implemented and discuss the strengths and weaknesses of each approach. Furthermore, we will detail and compare the usage of inconspicuous carrier protocols for covert communication channels in malware. Examples will span commodity cybercrime as well as targeted attack malware.
The cases that are discussed in this presentation are based on real life incidents. While it is easy to speculate how covert communication channels might be used by malicious actors, documentation of real-world cases is sparse. Yet covert communication channels have arrived in both, the commodity cybercrime and targeted attack world. It is thus vital to understand the status-quo and identify current trends in cybercriminal and targeted attack malware. As such, we believe that it is mandatory to highlight what is currently being used in the wild.