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With the frequency and impact of data breaches raising, it has become essential for organizations to automate intrusion detection via machine learning solutions. This generally comes with numerous challenges, among others high class imbalance, changing target concepts and difficulties to conduct sound evaluation. In this thesis, we adopt a user-centered anomaly detection perspective to address selected challenges of intrusion detection, through a real-world use case in the identity and access management (IAM) domain. In addition to the previous challenges, salient properties of this particular problem are high relevance of categorical data, limited feature availability and total absence of ground truth.
First, we ask how to apply anomaly detection to IAM audit logs containing a restricted set of mixed (i.e. numeric and categorical) attributes. Then, we inquire how anomalous user behavior can be separated from normality, and this separation evaluated without ground truth. Finally, we examine how the lack of audit data can be alleviated in two complementary settings. On the one hand, we ask how to cope with users without relevant activity history ("cold start" problem). On the other hand, we seek how to extend audit data collection with heterogeneous attributes (i.e. categorical, graph and text) to improve insider threat detection.
After aggregating IAM audit data into sessions, we introduce and compare general anomaly detection methods for mixed data to a user identification approach, designed to learn the distinction between normal and malicious user behavior. We find that user identification outperforms general anomaly detection and is effective against masquerades. An additional clustering step allows to reduce false positives among similar users. However, user identification is not effective against insider threats. Furthermore, results suggest that the current scope of our audit data collection should be extended.
In order to tackle the "cold start" problem, we adopt a zero-shot learning approach. Focusing on the CERT insider threat use case, we extend an intrusion detection system by integrating user relations to organizational entities (like assignments to projects or teams) in order to better estimate user behavior and improve intrusion detection performance. Results show that this approach is effective in two realistic scenarios.
Finally, to support additional sources of audit data for insider threat detection, we propose a method representing audit events as graph edges with heterogeneous attributes. By performing detection at fine-grained level, this approach advantageously improves anomaly traceability while reducing the need for aggregation and feature engineering. Our results show that this method is effective to find intrusions in authentication and email logs.
Overall, our work suggests that masquerades and insider threats call for different detection methods. For masquerades, user identification is a promising approach. To find malicious insiders, graph features representing user context and relations to other entities can be informative. This opens the door for tighter coupling of intrusion detection with user identities, roles and privileges used in IAM solutions.
The current movement towards a smart grid serves as a solution to present power grid challenges by introducing numerous monitoring and communication technologies. A dependable, yet timely exchange of data is on the one hand an existential prerequisite to enable Advanced Metering Infrastructure (AMI) services, yet on the other a challenging endeavor, because the increasing complexity of the grid fostered by the combination of Information and Communications Technology (ICT) and utility networks inherently leads to dependability challenges.
To be able to counter this dependability degradation, current approaches based on high-reliability hardware or physical redundancy are no longer feasible, as they lead to increased hardware costs or maintenance, if not both. The flexibility of these approaches regarding vendor and regulatory interoperability is also limited. However, a suitable solution to the AMI dependability challenges is also required to maintain certain regulatory-set performance and Quality of Service (QoS) levels.
While a part of the challenge is the introduction of ICT into the power grid, it also serves as part of the solution. In this thesis a Network Functions Virtualization (NFV) based approach is proposed, which employs virtualized ICT components serving as a replacement for physical devices. By using virtualization techniques, it is possible to enhance the performability in contrast to hardware based solutions through the usage of virtual replacements of processes that would otherwise require dedicated hardware. This approach offers higher flexibility compared to hardware redundancy, as a broad variety of virtual components can be spawned, adapted and replaced in a short time. Also, as no additional hardware is necessary, the incurred costs decrease significantly. In addition to that, most of the virtualized components are deployed on Commercial-Off-The-Shelf (COTS) hardware solutions, further increasing the monetary benefit.
The approach is developed by first reviewing currently suggested solutions for AMIs and related services. Using this information, virtualization technologies are investigated for their performance influences, before a virtualized service infrastructure is devised, which replaces selected components by virtualized counterparts. Next, a novel model, which allows the separation of services and hosting substrates is developed, allowing the introduction of virtualization technologies to abstract from the underlying architecture. Third, the performability as well as monetary savings are investigated by evaluating the developed approach in several scenarios using analytical and simulative model analysis as well as proof-of-concept approaches. Last, the practical applicability and possible regulatory challenges of the approach are identified and discussed.
Results confirm that—under certain assumptions—the developed virtualized AMI is superior to the currently suggested architecture. The availability of services can be severely increased and network delays can be minimized through centralized hosting. The availability can be increased from 96.82% to 98.66% in the given scenarios, while decreasing the costs by over 60% in comparison to the currently suggested AMI architecture. Lastly, the performability analysis of a virtualized service prototype employing performance analysis and a Musa-Okumoto approach reveals that the AMI requirements are fulfilled.
Fundamental changes in business-to-business (B2B) buying behavior confront B2B supplier firms with unprecedented challenges. On the one hand, a rising share of industrial buyers demands digitalized offerings and processes from suppliers. Consequently, suppliers are urged to implement digital transformations by expanding the range of both digital offerings and processes. On the other hand, B2B buyers increasingly expect suppliers to provide individually tailored solutions to their idiosyncratic needs. Hence, suppliers are also required to implement non-digital transformations by providing offerings and processes that are customized to each customers’ specific requirements.
The rise of these digital and non-digital transformations calls established knowledge into question. Thus, B2B marketing research and practice are urged to create a comprehensive understanding of digital and non-digital transformations by means of novel and empirically grounded insights and derive actionable response strategies. In respond, my dissertation addresses the overall research question of how B2B supplier firms can successfully implement both digital and non-digital transformations in three individual essays.
In Essay 1, I offer a broader perspective on both digital and non-digital transformations by investigating digital service customization (i.e., the tailoring of digital B2B services to customers’ individual needs). Through a systematic literature review and bibliometric analysis, I outline a comprehensive set of factors that favor the application of distinct digital service customization strategies. Essay 2 represents a deep dive into digital transformations of sales processes. By making use of two rich sets of qualitative interview material from supplier and buyer firms, I identify the challenges resulting for B2B salespeople from the introduction of digital sales channels into personal selling. Moreover, I uncover facilitating mechanisms that sales managers can employ to support salespeople in coping with digital sales channels. Finally, Essay 3 constitutes a deep dive into non-digital transformations. Based on qualitative interview material and survey data from matched sales manager–salesperson dyads, the essay explores how configurations of individual salespeople’s personal and procedural competencies facilitate success at selling customer solutions (i.e., highly customized, performance-oriented offerings comprising products and/or services). The essay shows that successfully selling customized offerings like solutions hinges on salespeople’s unique configurations of present and absent competencies.
In a nutshell, these essays provide three major insights on how B2B suppliers can successfully implement digital and non-digital transformations. First, they underscore that a comprehensive understanding of the origins and spillover effects of transformations is a key prerequisite to successfully implementing them. Second, they unveil that digital and non-digital transformations impact on multiple organizational levels. Third, they point out important resources and capabilities that help suppliers to successfully implement transformations, be they digital or non-digital.
With this dissertation, I make substantial contributions to the broader literature on digital and non-digital transformations in B2B contexts. At the same time, my dissertation provides hands-on implications for managers in B2B supplier firms that are facing fundamental transformations in the marketplace—both digital and non-digital in nature.
A plethora of resources made available via retrieval systems in digital libraries remains untapped in the so called long tail of the Web. These long-tail websites get considerably less visits than major Web hubs.
Zero-effort queries ease the discovery of long-tail resources by proactively retrieving and presenting information based on a user’s context. However, zero-effort queries over existing digital library structures are challenging, since the underlying retrieval system is only accessible via an API. The information need must be expressed by a query, instead of optimizing the ranking between context and resources in the retrieval system directly. We address three research questions that arise from replacing the user information seeking process by zero-effort queries.
Our first question addresses the transformation of a user query to an automatic query, derived from the context. We present means to 1) identify the relevant context on different levels of granularity, 2) derive an information need from the context via keyword extraction and personalization and 3) express this information need in a query scheme that avoids over- or under-specified queries. We address the cold start problem with an approach to bootstrap user profiles from social media, even for passive users.
With the second question, we address the presentation of resources in zero-effort query scenarios, presenting guidelines for presentation interfaces in the browser and a visualization of the triadic relationship between context, query and results. QueryCrumbs, a compact query history visualization supports recalling information found in the past and exploratory search by visualizing qualitative and quantitative query similarity.
Our last question addresses the gap between (simple) keyword queries and the representation of resources by rich and complex meta-data. We investigate and extend feature representation learning techniques centered around the skip-gram model with negative sampling. Finally, we present an approach to learn representations from network and text jointly that can cope with the partial absence of one modality.
Experimental results show close to human performance of our zero-effort query and user profile generation approach and visualizations to be helpful in terms of transparency, efficiency and support for exploratory search. These results indicate that the proposed zero-effort query approach indeed eases the discovery of long-tail resources and the accompanying visualizations further facilitate this process. The joint representation model provides a first step to bridge the gap between query and resource representation and we plan to follow and investigate this route further in the future.
Our subject of study is strong approximation of stochastic differential equations (SDEs) with respect to the supremum and the L_p error criteria, and we seek approximations that are strongly asymptotically optimal in specific classes of approximations. For the supremum error, we prove strong asymptotic optimality for specific tamed Euler schemes relating to certain adaptive and to equidistant time discretizations. For the L_p error, we prove strong asymptotic optimality for specific tamed Milstein schemes relating to certain adaptive and to equidistant time discretizations. To illustrate our findings, we numerically analyze the SDE associated with the Heston–3/2–model originating from mathematical finance.
The current electricity grid is undergoing major changes. There is increasing pressure to move away from power generation from fossil fuels, both due to ecological concerns and fear of dependencies on scarce natural resources. Increasing the share of decentralized generation from renewable sources is a widely accepted way to a more sustainable power infrastructure. However, this comes at the price of new challenges: generation from solar or wind power is not controllable and only forecastable with limited accuracy. To compensate for the increasing volatility in power generation, exerting control on the demand side is a promising approach. By providing flexibility on demand side, imbalances between power generation and demand may be mitigated.
This work is concerned with developing methods to provide grid support on demand side while limiting the associated costs. This is done in four major steps: first, the target power curve to follow is derived taking both goals of a grid authority and costs of the respective load into account. In the following, the special case of data centers as an instance of significant loads inside a power grid are focused on more closely. Data center services are adapted in a way such as to achieve the previously derived power curve. By means of hardware power demand models, the required adaptation of hardware utilization can be derived. The possibilities of adapting software services are investigated for the special use case of live video encoding. A method to minimize quality of experience loss while reducing power demand is presented. Finally, the possibility of applying probabilistic model checking to a continuous demand-response scenario is demonstrated.
We consider a number of enhancements to the standard neural network training paradigm. First, we show that carefully designed parameter update rules may replace the need for a loss function and its gradient. We introduce a parameter update rule that generalises the standard cross-entropy gradient, and allows directly controlling the relative effect of easy and hard examples on the training process. We show that the proposed update rule cannot be derived by using a loss function and yields better classification accuracy compared to training with the standard cross-entropy loss.
In addition, we study the effect of the loss function choice on the learnt representations. We introduce the Single Logit Classification (SLC) task: classifying whether a given class is the correct class for a given example, in a computationally efficient manner, based on the appropriate class logit alone. A natural principle is proposed, the Principle of Logit Separation (PoLS), as a guideline for choosing and designing loss functions suitable for the SLC task. We mathematically analyse the alignment of eleven existing and novel loss functions with this principle. Experiment results show that using loss functions that are aligned with this principle results in a representation in the logits layer in which each logit is more informative of its class correctness, leading to a considerably better SLC accuracy.
Further, we attempt to alleviate the dependency of standard neural network models on large amounts of quality labels. The task of weakly supervised one-shot detection is considered, in which at training time the model is trained without any localisation labels, and at test time it needs to identify and localise instances of unseen classes. We propose the attention similarity networks (ASN) for this task. ASN use a Siamese neural network to compute a similarity score between an exemplar and different locations in a target example. Then, an attention mechanism performs localisation by learning to attend to the correct locations. The ASN model outperforms the relevant baselines for weakly supervised one-shot detection tasks in the audio and computer vision domains.
Finally, we consider the problem of quantifying prediction confidence in the regression setting. We propose two novel algorithms for emitting calibrated prediction intervals for neural network regressors, at any given confidence level. The two algorithms require binning of the output space and training the neural network regressor as a classifier. Then, the calibration algorithms choose the intervals in the output space, making sure they contain the amount of posterior probability mass that results in the desired confidence level.
Cryptography is the scientific study of techniques for securing information and communication against adversaries. It is about designing and analyzing encryption schemes and protocols that protect data from unauthorized reading. However, in our modern information-driven society with highly complex and interconnected information systems, encryption alone is no longer enough as it makes the data unintelligible, preventing any meaningful computation without decryption. On the one hand, data owners want to maintain control over their sensitive data. On the other hand, there is a high business incentive for collaborating with an untrusted external party.
Modern cryptography encompasses different techniques, such as secure multiparty computation, homomorphic encryption or order-preserving encryption, that enable cloud users to encrypt their data before outsourcing it to the cloud while still being able to process and search on the outsourced and encrypted data without decrypting it. In this thesis, we rely on these cryptographic techniques for computing on encrypted data to propose efficient multiparty protocols for order-preserving encryption, decision tree evaluation and kth-ranked element computation.
We start with Order-preserving encryption (OPE) which allows encrypting data, while still enabling efficient range queries on the encrypted data. However, OPE is symmetric limiting, the use case to one client and one server. Imagine a scenario where a Data Owner (DO) outsources encrypted data to the Cloud Service Provider (CSP) and a Data Analyst (DA) wants to execute private range queries on this data. Then either the DO must reveal its encryption key or the DA must reveal the private queries. We overcome this limitation by allowing the equivalent of a public-key OPE.
Decision trees are common and very popular classifiers because they are explainable. The problem of evaluating a private decision tree on private data consists of a server holding a private decision tree and a client holding a private attribute vector. The goal is to classify the client’s input using the server’s model such that the client learns only the result of the classification, and the server learns nothing. In a first approach, we represent the tree as an array and execute only d interactive comparisons (instead of 2 d as in existing solutions), where d denotes the depth of the tree. In a second approach, we delegate the complete tree evaluation to the server using somewhat or fully homomorphic encryption where the ciphertexts are encrypted under the client’s public key.
A generalization of a decision tree is a random forest that consists of many decision trees. A classification with a random forest evaluates each decision tree in the forest and outputs the classification label which occurs most often. Hence, the classification labels are ranked by their number of occurrences and the final result is the best ranked one. The best ranked element is a special case of the kth-ranked element. In this thesis, we consider the secure computation of the kth-ranked element in a distributed setting with applications in benchmarking and auctions. We propose different approaches for privately computing the kth-ranked element in a star network, using either garbled circuits or threshold homomorphic encryption.
Job Sequencing and Tool Switching Problems with a Generalisation to Non-Identical Parallel Machines
(2020)
Manufacturing tools have been dominating the manufacturing process since the 1960s. The job sequencing and tool switching problem is an NP-hard combinatorial optimization that has first been introduced in the context of flexible manufacturing systems in the late 1980s. Since then, production systems have undisputedly changed and improved but manufacturing tools still dominate manufacturing processes. Production and system operation processes are continuously adjusted and optimised to changing customer requirements. If the product variety requires an increasing number of tools for processing that exceeds the local tool magazine capacity of the manufacturing system, tool switches become necessary. Although tool changing times within a manufacturing centre or cell may nowadays be very small due to the high degree of automation, tool switching within a dynamic production environment is still a time consuming process that must be avoided. In order to minimize the total tool setup time to enhance productivity, the objectives of the basic job sequencing and tool switching problem are to sequence a set of jobs and simultaneously to determine the best tool loading. Therefore, job sequencing and tool switching problems are gaining considerable attention.
Several solution approaches to the standard problem and related versions of the problem exist. The first part of this dissertation assesses the current state-of-the-art of the job sequencing and tool switching problem and provides a classification scheme for literature on the job sequencing and tool switching problem and its variations. Only few authors consider generalisations of the problem because the level of complexity of extended problems is high. A general approach of the job sequencing and tool switching problem with non-identical parallel machines and sequence-dependent setup times is described in this dissertation. A novel mathematical model based on time periods is presented and analysed which can be adapted to different objective functions. The last part of this dissertation is a quantitative evaluation of fast and effective construction heuristics as well as of an iterated local search algorithm tested on a new set of benchmark instances. As such this dissertation provides a broad basis for future evaluations of solution approaches to the job sequencing and tool switching problem with non-identical parallel machines and sequence-dependent setup times as well as a basis for further generalisations of the problem like for example tool availability constraints or tool-size dependent variations.
Das Ziel der vorliegenden Dissertation ist es, Erkenntnisse für eine mögliche Verbesserung des vietnamesischen Zwangsvollstreckungsrechts zu gewinnen, um in Vietnam ein effektives und effizientes Vollstreckungsverfahren zu erreichen, das im Einklang mit den internationalen Standards steht. Das erste Kapitel untersucht die neuen internationalen Standards im Bereich der Vollstreckung von Zivilurteilen und behandelt sie im Vergleich mit wichtigen Grundsätzen der Vollstreckung von Gerichtsurteilen in Vietnam. Der zweite Kapitel widmet sich dem Aufbau und der Organisation der Vollstreckungsbehörden und dabei insbesondere den folgenden Themen: Den Vorteilen des Aufbaus eines Berufsverbands der Gerichtsvollzieher, welcher alle Mitglieder des Berufsstandes umfasst. Das dritte Kapitel zeigt unter anderem, dass wirksame Mechanismen zur Vollstreckung von Entschreidungen den Grundsatz der Verhältnismäßigkeit einhalten müssen. Das vierte Kapitel stellt die internationalen Normen über den einstweiligen Rechtsschutz dar, der ein unverzichtbares Mittel ist, um die Durchsetzung von Zivilurteilen zu gewährleisten.
Basierend auf den Ergebnissen aus den vier Kapiteln ergeben sich eine Reihe von wertvollen Erkenntnissen für die Verbesserung des vietnamesischen Rechtssystems und die Verbesserung der Effizienz der Vollstreckung zivilgerichtlicher Urteile.