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The identification of vulnerabilities in IT infrastructures is a crucial problem in enhancing the security, because many incidents resulted from already known vulnerabilities, which could have been resolved. Thus, the initial identification of vulnerabilities has to be used to directly resolve the related weaknesses and mitigate attack possibilities. The nature of vulnerability information requires a collection and normalization of the information prior to any utilization, because the information is widely distributed in different sources with their unique formats. Therefore, the comprehensive vulnerability model was defined and different sources have been integrated into one database. Furthermore, different analytic approaches have been designed and implemented into the HPI-VDB, which directly benefit from the comprehensive vulnerability model and especially from the logical preconditions and postconditions.
Firstly, different approaches to detect vulnerabilities in both IT systems of average users and corporate networks of large companies are presented. Therefore, the approaches mainly focus on the identification of all installed applications, since it is a fundamental step in the detection. This detection is realized differently depending on the target use-case. Thus, the experience of the user, as well as the layout and possibilities of the target infrastructure are considered. Furthermore, a passive lightweight detection approach was invented that utilizes existing information on corporate networks to identify applications.
In addition, two different approaches to represent the results using attack graphs are illustrated in the comparison between traditional attack graphs and a simplistic graph version, which was integrated into the database as well. The implementation of those use-cases for vulnerability information especially considers the usability. Beside the analytic approaches, the high data quality of the vulnerability information had to be achieved and guaranteed. The different problems of receiving incomplete or unreliable information for the vulnerabilities are addressed with different correction mechanisms. The corrections can be carried out with correlation or lookup mechanisms in reliable sources or identifier dictionaries. Furthermore, a machine learning based verification procedure was presented that allows an automatic derivation of important characteristics from the textual description of the vulnerabilities.
Medical imaging plays an important role in disease diagnosis, treatment planning, and clinical monitoring. One of the major challenges in medical image analysis is imbalanced training data, in which the class of interest is much rarer than the other classes. Canonical machine learning algorithms suppose that the number of samples from different classes in the training dataset is roughly similar or balance. Training a machine learning model on an imbalanced dataset can introduce unique challenges to the learning problem.
A model learned from imbalanced training data is biased towards the high-frequency samples. The predicted results of such networks have low sensitivity and high precision. In medical applications, the cost of misclassification of the minority class could be more than the cost of misclassification of the majority class. For example, the risk of not detecting a tumor could be much higher than referring to a healthy subject to a doctor. The current Ph.D. thesis introduces several deep learning-based approaches for handling class imbalanced problems for learning multi-task such as disease classification and semantic segmentation.
At the data-level, the objective is to balance the data distribution through re-sampling the data space: we propose novel approaches to correct internal bias towards fewer frequency samples. These approaches include patient-wise batch sampling, complimentary labels, supervised and unsupervised minority oversampling using generative adversarial networks for all.
On the other hand, at algorithm-level, we modify the learning algorithm to alleviate the bias towards majority classes. In this regard, we propose different generative adversarial networks for cost-sensitive learning, ensemble learning, and mutual learning to deal with highly imbalanced imaging data.
We show evidence that the proposed approaches are applicable to different types of medical images of varied sizes on different applications of routine clinical tasks, such as disease classification and semantic segmentation. Our various implemented algorithms have shown outstanding results on different medical imaging challenges.
In the era of social networks, internet of things and location-based services, many online services produce a huge amount of data that have valuable objective information, such as geographic coordinates and date time. These characteristics (parameters) in the combination with a textual parameter bring the challenge for the discovery of geospatiotemporal knowledge. This challenge requires efficient methods for clustering and pattern mining in spatial, temporal and textual spaces.
In this thesis, we address the challenge of providing methods and frameworks for geospatiotemporal data analytics. As an initial step, we address the challenges of geospatial data processing: data gathering, normalization, geolocation, and storage. That initial step is the basement to tackle the next challenge -- geospatial clustering challenge. The first step of this challenge is to design the method for online clustering of georeferenced data. This algorithm can be used as a server-side clustering algorithm for online maps that visualize massive georeferenced data. As the second step, we develop the extension of this method that considers, additionally, the temporal aspect of data. For that, we propose the density and intensity-based geospatiotemporal clustering algorithm with fixed distance and time radius.
Each version of the clustering algorithm has its own use case that we show in the thesis.
In the next chapter of the thesis, we look at the spatiotemporal analytics from the perspective of the sequential rule mining challenge. We design and implement the framework that transfers data into textual geospatiotemporal data - data that contain geographic coordinates, time and textual parameters. By this way, we address the challenge of applying pattern/rule mining algorithms in geospatiotemporal space. As the applicable use case study, we propose spatiotemporal crime analytics -- discovery spatiotemporal patterns of crimes in publicly available crime data.
The second part of the thesis, we dedicate to the application part and use case studies. We design and implement the application that uses the proposed clustering algorithms to discover knowledge in data. Jointly with the application, we propose the use case studies for analysis of georeferenced data in terms of situational and public safety awareness.
With the emergence of the Internet of things (IoT), plenty of battery-powered and energy-harvesting devices are being deployed to fulfill sensing and actuation tasks in a variety of application areas, such as smart homes, precision agriculture, smart cities, and industrial automation. In this context, a critical issue is that of denial-of-sleep attacks. Such attacks temporarily or permanently deprive battery-powered, energy-harvesting, or otherwise energy-constrained devices of entering energy-saving sleep modes, thereby draining their charge. At the very least, a successful denial-of-sleep attack causes a long outage of the victim device. Moreover, to put battery-powered devices back into operation, their batteries have to be replaced. This is tedious and may even be infeasible, e.g., if a battery-powered device is deployed at an inaccessible location. While the research community came up with numerous defenses against denial-of-sleep attacks, most present-day IoT protocols include no denial-of-sleep defenses at all, presumably due to a lack of awareness and unsolved integration problems. After all, despite there are many denial-of-sleep defenses, effective defenses against certain kinds of denial-of-sleep attacks are yet to be found.
The overall contribution of this dissertation is to propose a denial-of-sleep-resilient medium access control (MAC) layer for IoT devices that communicate over IEEE 802.15.4 links. Internally, our MAC layer comprises two main components. The first main component is a denial-of-sleep-resilient protocol for establishing session keys among neighboring IEEE 802.15.4 nodes. The established session keys serve the dual purpose of implementing (i) basic wireless security and (ii) complementary denial-of-sleep defenses that belong to the second main component. The second main component is a denial-of-sleep-resilient MAC protocol. Notably, this MAC protocol not only incorporates novel denial-of-sleep defenses, but also state-of-the-art mechanisms for achieving low energy consumption, high throughput, and high delivery ratios. Altogether, our MAC layer resists, or at least greatly mitigates, all denial-of-sleep attacks against it we are aware of. Furthermore, our MAC layer is self-contained and thus can act as a drop-in replacement for IEEE 802.15.4-compliant MAC layers. In fact, we implemented our MAC layer in the Contiki-NG operating system, where it seamlessly integrates into an existing protocol stack.
Creating fonts is a complex task that requires expert knowledge in a variety of domains. Often, this knowledge is not held by a single person, but spread across a number of domain experts. A central concept needed for designing fonts is the glyph, an elemental symbol representing a readable character. Required domains include designing glyph shapes, engineering rules to combine glyphs for complex scripts and checking legibility. This process is most often iterative and requires communication in all directions. This report outlines a platform that aims to enhance the means of communication, describes our prototyping process, discusses complex font rendering and editing in a live environment and an approach to generate code based on a user’s live-edits.
Graph repair, restoring consistency of a graph, plays a prominent role in several areas of computer science and beyond: For example, in model-driven engineering, the abstract syntax of models is usually encoded using graphs. Flexible edit operations temporarily create inconsistent graphs not representing a valid model, thus requiring graph repair. Similarly, in graph databases—managing the storage and manipulation of graph data—updates may cause that a given database does not satisfy some integrity constraints, requiring also graph repair. We present a logic-based incremental approach to graph repair, generating a sound and complete (upon termination) overview of least-changing repairs. In our context, we formalize consistency by so-called graph conditions being equivalent to first-order logic on graphs. We present two kind of repair algorithms: State-based repair restores consistency independent of the graph update history, whereas deltabased (or incremental) repair takes this history explicitly into account. Technically, our algorithms rely on an existing model generation algorithm for graph conditions implemented in AutoGraph. Moreover, the delta-based approach uses the new concept of satisfaction (ST) trees for encoding if and how a graph satisfies a graph condition. We then demonstrate how to manipulate these STs incrementally with respect to a graph update.
Graph repair, restoring consistency of a graph, plays a prominent role in several areas of computer science and beyond: For example, in model-driven engineering, the abstract syntax of models is usually encoded using graphs. Flexible edit operations temporarily create inconsistent graphs not representing a valid model, thus requiring graph repair. Similarly, in graph databases—managing the storage and manipulation of graph data—updates may cause that a given database does not satisfy some integrity constraints, requiring also graph repair.
We present a logic-based incremental approach to graph repair, generating a sound and complete (upon termination) overview of least-changing repairs. In our context, we formalize consistency by so-called graph conditions being equivalent to first-order logic on graphs. We present two kind of repair algorithms: State-based repair restores consistency independent of the graph update history, whereas deltabased (or incremental) repair takes this history explicitly into account. Technically, our algorithms rely on an existing model generation algorithm for graph conditions implemented in AutoGraph. Moreover, the delta-based approach uses the new concept of satisfaction (ST) trees for encoding if and how a graph satisfies a graph condition. We then demonstrate how to manipulate these STs incrementally with respect to a graph update.
With the growth of information technology, patient attitudes are shifting – away from passively receiving care towards actively taking responsibility for their well- being. Handling doctor-patient relationships collaboratively and providing patients access to their health information are crucial steps in empowering patients. In mental healthcare, the implicit consensus amongst practitioners has been that sharing medical records with patients may have an unpredictable, harmful impact on clinical practice. In order to involve patients more actively in mental healthcare processes, Tele-Board MED (TBM) allows for digital collaborative documentation in therapist-patient sessions. The TBM software system offers a whiteboard-inspired graphical user interface that allows therapist and patient to jointly take notes during the treatment session. Furthermore, it provides features to automatically reuse the digital treatment session notes for the creation of treatment session summaries and clinical case reports. This thesis presents the development of the TBM system and evaluates its effects on 1) the fulfillment of the therapist’s duties of clinical case documentation, 2) patient engagement in care processes, and 3) the therapist-patient relationship. Following the design research methodology, TBM was developed and tested in multiple evaluation studies in the domains of cognitive behavioral psychotherapy and addiction care. The results show that therapists are likely to use TBM with patients if they have a technology-friendly attitude and when its use suits the treatment context. Support in carrying out documentation duties as well as fulfilling legal requirements contributes to therapist acceptance. Furthermore, therapists value TBM as a tool to provide a discussion framework and quick access to worksheets during treatment sessions. Therapists express skepticism, however, regarding technology use in patient sessions and towards complete record transparency in general. Patients expect TBM to improve the communication with their therapist and to offer a better recall of discussed topics when taking a copy of their notes home after the session. Patients are doubtful regarding a possible distraction of the therapist and usage in situations when relationship-building is crucial. When applied in a clinical environment, collaborative note-taking with TBM encourages patient engagement and a team feeling between therapist and patient. Furthermore, it increases the patient’s acceptance of their diagnosis, which in turn is an important predictor for therapy success. In summary, TBM has a high potential to deliver more than documentation support and record transparency for patients, but also to contribute to a collaborative doctor-patient relationship. This thesis provides design implications for the development of digital collaborative documentation systems in (mental) healthcare as well as recommendations for a successful implementation in clinical practice.
While the IEEE 802.15.4 radio standard has many features that meet the requirements of Internet of things applications, IEEE 802.15.4 leaves the whole issue of key management unstandardized. To address this gap, Krentz et al. proposed the Adaptive Key Establishment Scheme (AKES), which establishes session keys for use in IEEE 802.15.4 security. Yet, AKES does not cover all aspects of key management. In particular, AKES comprises no means for key revocation and rekeying. Moreover, existing protocols for key revocation and rekeying seem limited in various ways. In this paper, we hence propose a key revocation and rekeying protocol, which is designed to overcome various limitations of current protocols for key revocation and rekeying. For example, our protocol seems unique in that it routes around IEEE 802.15.4 nodes whose keys are being revoked. We successfully implemented and evaluated our protocol using the Contiki-NG operating system and aiocoap.
The ability to work in teams is an important skill in today's work environments. In MOOCs, however, team work, team tasks, and graded team-based assignments play only a marginal role. To close this gap, we have been exploring ways to integrate graded team-based assignments in MOOCs. Some goals of our work are to determine simple criteria to match teams in a volatile environment and to enable a frictionless online collaboration for the participants within our MOOC platform. The high dropout rates in MOOCs pose particular challenges for team work in this context. By now, we have conducted 15 MOOCs containing graded team-based assignments in a variety of topics. The paper at hand presents a study that aims to establish a solid understanding of the participants in the team tasks. Furthermore, we attempt to determine which team compositions are particularly successful. Finally, we examine how several modifications to our platform's collaborative toolset have affected the dropout rates and performance of the teams.