Current digital payment solutions are fragile and offer less privacy than traditional cash. Their critical dependency on an online service used to perform and validate transactions makes them void if this service is unreachable. Moreover, no transaction can be executed during server malfunctions or power outages. Due to climate change, the likelihood of extreme weather increases. As extreme weather is a major cause of power outages, the frequency of power outages is expected to increase. The lack of privacy is an inherent result of their account-based design or the use of a public ledger. The critical dependency and lack of privacy can be resolved with a Central Bank Digital Currency that can be used offline. This thesis proposes a design and a first implementation for an offline-first digital euro. The protocol offers complete privacy during transactions using zero-knowledge proofs. Furthermore, transactions can be executed offline without third parties and retroactive double-spending detection is facilitated. To protect the users’ privacy, but also guard against money laundering, we have added the following privacy-guarding mechanism. The bank and trusted third parties for law enforcement must collaborate to decrypt transactions, revealing the digital pseudonym used in the transaction. Importantly, the transaction can be decrypted without decrypting prior transactions attached to the
digital euro. The protocol has a working initial implementation showcasing its usability and demonstrating functionality.

This thesis researches the security of firmware images in the Internet of Things (IoT) and embedded devices. We present an open-source tool, Embedded Binary Analysis Tool (EBAT), designed to analyze cross-architectural firmware image security context. EBAT consists of various modules capable of discovering outdated software for various libraries, particularly on cryptographic libraries, and detecting Common Vulnerabilities and Exposures (CVEs), focusing on firmware's cryptographic libraries. It also detects exploit mitigation techniques on firmware's image binaries and discovers credentials and passwords with a focus on private keys embedded in the firmware image. Additionally, EBAT identifies Application Programming Interfaces (APIs) cryptographic misuses through static taint analysis (backward tracking) on cross-architectural binaries. We presented a total of 18 well-defined cryptographic rules and a list of 733 function calls with more than 1,600 function arguments, applicable in static taint analysis to check the possibility of cryptographic misuses based on 10 well-used open-source cryptographic libraries APIs. EBAT's static taint analysis provides a powerful framework for detecting the possibility of cryptographic misuses in cross-architectural binaries, making it a valuable tool for identifying and addressing vulnerabilities in cryptographic implementation in firmware images.

Using EBAT, we conducted a large-scale analysis of over 36,000 firmware images publicly crawled from the Internet and successfully unpacked over 60% of them. The created dataset of firmware images includes more than 5,000 different products across 33 vendors, spanning more than 20 years and a plethora of various device types. Our findings show that ARM and MIPS are the most prevailed CPU architecture in the IoT/embedded industry. We compared identical binaries across all vendors, revealing a significant percentage of similar binaries used across different vendors' firmware images. Our analysis of firmware binaries reveals a notable absence of exploit mitigation techniques in IoT/embedded firmware images, and we present many firmware images containing private keys, posing potential security threats. Additionally, versions of open-source cryptographic libraries used in firmware images are identified, and the CVEs of the cryptographic libraries are evaluated. Two real-world case studies on hard-coded credentials demonstrate the significance of the large-scale attack presented in this thesis. Hashed passwords, predominantly using outdated algorithms, have also been discovered, and several have been cracked.

The main goal of EBAT is to identify cryptographic misuses in cross-architectural binaries. By applying static taint analysis (backward tracking) to well-defined APIs on specific functions and arguments for 10 open-source cryptographic libraries, we can identify potential violations of cryptographic rules. This analysis was executed on over 1.4 million binaries, revealing that approximately 50% of examined firmware images violated at least one cryptographic rule. Various case studies on real-world vulnerabilities in firmware images are presented, including recent CVEs that are found in various vendors' products. Executing EBAT on those vulnerable firmware images, we tested the effectiveness of our tool to evaluate the automatic capturing of these known vulnerabilities. In addition, performing large-scale analysis on an extensive corpus of firmware images allows us to discover that other firmware images are affected by these known vulnerabilities, in some cases also across various product lines not covered on the public CVEs reports.

In conclusion, EBAT is a valuable resource for researchers working on firmware security. Its automated analysis process, comprehensive modules, and ability to discover possible vulnerabilities, cryptographic misuses at a binary level, and other security weaknesses make it a powerful tool for identifying and mitigating security risks in IoT/embedded devices.

The Dutch railway system is one of the most densely used systems worldwide and the busiest in Europe. Given the tight schedules, incidents can quickly cascade through the entire country if not handled properly. Alternative timetables are created to help train traffic controllers swiftly resolve such incidents. These schedules are currently created manually, but the team cannot keep up with demand. This is why CGI is developing VGB Solver, an application to automatically generate such timetables. The solver uses a branch and bound algorithm in which nodes are processed in best-first order, based on a heuristic value. For this thesis, different performance optimisations for this algorithm were implemented and analysed.

Various alternative formulations of the heuristic value, used to determine the likeliness of a node leading to a good solution, showed promising results.
One of these formulas resulted in solutions for 96% more scenarios than before, and improved the quality of solutions for other scenarios.

Using machine learning, a decision tree was created to predict whether applying another new formula for the heuristic value gives better results than the old formula. This classifier achieved an accuracy of 73.5% on the test data. Having the solver choose between the old and new formula based on that classification resulted in some scenarios with worse scores, but twice as many improved, and the average improvement was higher than the average deterioration.

Recommendations are made to conduct further experiments related to the heuristic value calculation. Furthermore, it is suggested to separate the scores for evaluating solution quality from the heuristic value formula, to facilitate more fine-grained changes to the calculation of the heuristic value.

Foremost among the challenges of the Bitcoin blockchain is the scalability bottleneck. To address this issue, the Lightning Network, a payment channel network, was created. Lightning is a payment channel network that is source-routed and uses onion routing, like Tor. However, unlike Tor, the routing path is determined by optimizing a cost function, which uses public information. In this paper, a timing attack is evaluated by simulation of the Lightning Network. The goal of the attack is for an adversary that is part of a payment to determine the destination of the payment. The influence colluding adversaries have on the performance of the attack is evaluated. Three types of colluding adversaries (closest to destination, farthest from destination, average of all adversaries) are compared to a simple adversary that guesses the next hop in the payment is the destination of the payment. It is found that the adversary closest to the destination performs the best. Furthermore, shadow routing, a mitigation against these types of attacks, is evaluated against this attack. It is found that shadow routing does not have a significant impact on the performance of this attack.

Call graphs are useful tools for representing method relationships within software projects and correlations between dependencies. Although static analysis is a prevalent method for call graph construction, it has its limitations such as struggling with handling dynamic features and lambda expressions. In this research, we introduced an approach that utilizes test suites from public Java Maven projects to construct dynamic call graphs and then merge them with static call graphs. Our objective is to explore the efficacy of the merged call graph in uncovering additional information. We employed OPAL and Soot to generate static call graphs and Java agents to trace edges during actual execution. Subsequently, a merging procedure, coupled with a filtering mechanism, was implemented to eliminate duplications. We conducted vulnerability detection analysis to assess the results and a version analysis to investigate the potential for extending our approach by merging multiple versions. Our results indicated that the merged call graph offers a modest increase in edges compared to solely static analysis. Additionally, we discovered that vulnerability identification followed a comparable pattern, supporting the consistency of our methodology. Additionally, we discover that combining several minor/patch versions of a project is a successful tactic for enhancing test coverage. Our research highlights the value of using test suites from open-source projects to build more in-depth call graphs.

Federated Learning is highly susceptible to backdoor and targeted attacks as participants can manipulate their data and models locally without any oversight on whether they follow the correct process. There are a number of server-side defenses that mitigate the attacks by modifying or rejecting local updates submitted by clients. However, we find that bursty adversarial patterns with a high variance in the number of malicious clients can circumvent the existing defenses. We propose a client-self defense, LeadFL, that is combined with existing server-side defenses to thwart backdoor and targeted attacks. The core idea of LeadFL is a novel regularization term in local model training such that the Hessian matrix of local gradients is nullified. We provide the convergence analysis of LeadFL and its robustness guarantee in terms of certified radius. Our empirical evaluation shows that LeadFL is able to mitigate bursty adversarial patterns for both iid and non-iid data distributions. It frequently reduces the backdoor accuracy from more than 75% for state-of-the-art defenses to less than 10% while its impact on the main task accuracy is always less than for other client-side defenses.

Machine learning models are increasingly being used within software engineering for their predictions. Research shows that these models’ performance is increasing with new research. This thesis focuses on models for method name prediction, for which the goal is to have a model that can accurately predict method names. With this thesis, we could create a tool that can suggest method names to software developers, which would assist in improving the quality of the projects.
This research aims to get insight into the robustness vulnerabilities of a method name prediction model. We use a genetic search algorithm that looks for these robustness problems. The main question this thesis tries to answer is to what extent the performance metrics are affected by applying metamorphic transformations to the test set of a trained code2vec model. Besides this, this thesis also proposes an alternative metric called percentage MRR, which might better reflect the robustness of a model. The main idea behind this metric is that it penalizes the prediction certainty of a model instead of penalizing the prediction rank.
To answer this research question, a tool is created that runs a genetic algorithm applying these metamorphic transformations to a dataset that a trained model is then evaluating. With this tool, we conducted 22 genetic search experiments on primary metrics and combinations of metrics to see the trade-offs in the Pareto fronts. The guided search of applying metamorphic transformations on the test set results in an average performance decrease of around 19%. This thesis also compares this drop in performance to the performance decrease a random search algorithm would create. Notably, for every transformer added, the average decrease in performance becomes smaller, and there are transformations, e.g., the if-false-else transformation, that have a bigger effect than others. This thesis concludes that the trained model is not robust against metamorphic transformations and has a significant performance drop.

Searchable Symmetric Encryption (SSE) schemes provide secure search over encrypted databases while allowing admitted information leakages. Generally, the leakages can be categorized into access, search, and volume pattern. In most existing Searchable Encryption (SE) schemes, these leakages are caused by practical designs but are considered an acceptable price to achieve high search efficiency. Many attacks on SSE schemes have shown that such leakages could be easily exploited to retrieve the underlying keywords for search queries. Each attack abuses a different leakage pattern and uses different techniques to achieve high query recovery accuracy. An attacker could be passive or active, where an active attacker can inject files in an SSE scheme, while a passive attacker only observes the queried data. Some passive attacks use the number of files returned by a query to create a match with a candidate keyword. Others use the co-occurrence of multiple keywords in the files to match a query with the same occurrence. We continue this research and design a new Volume and Access Pattern Leakage-abuse Attack (VAL-Attack) that exploits both the access and volume patterns. Our proposed attack only leverages leaked documents and the keywords present in those documents as auxiliary knowledge and can effectively retrieve document and keyword matches from leaked data. Furthermore, the recovery performs with great accuracy and without false positives. We compare VALAttack with two recent well-defined attacks on several real-world datasets to highlight the effectiveness of our attack and present the performance under popular countermeasures.

In this Thesis a fast, low-cost, anthropomorphic robotic hand with tactile feedback is designed. The hand consists of an index finger and a thumb, both of which have four degrees of freedom. All eight degrees of freedom are fully actuated using eight servomotors whose forces are transferred using tendons. The design introduces tendon routing to minimize the change in tendon tension as the joints rotate. To accomplish this at the 2 degree of freedom joints of thumb and index finger, a novel design for the ball and socket joint is created. The hand can be controlled using a tactile glove called the SenseGlove which also enables the user to receive force feedback. Testing reveals that the robot can open and close its fingers within 275 ms, and is able to grab a variety of objects.

In this paper, a method is proposed for stylizing stereoscopic augmented reality, while attempting to retain temporal and visual coherence. By tracking an AR marker and estimating a disparity map from stereo images, world positions of anchor points are tracked across frames and images, in order to move them with the scene. Additionally, the density of anchor points in the image is adjusted each frame to prevent areas from being over- or underpopulated with anchor points. The method is applied to a simple mosaic style and both quantitative and qualitative results demonstrate improved coherence when compared to a baseline. Besides that, the algorithm runs at near-interactive frame rates.

According to the Contrast Sensitivity Function, the contrast of different spatial frequency elements of an image will be seen differently depending on distance. We propose a method for compensating for these differences by acting on an image's frequency representation and manipulating the magnitudes of the individual wave functions composing the image. We provide an open source implementation of the algorithm which can be used to process videos and images in real time.

The appearance of an object or scene is determined by factors like the material, the lights, the geometry, the position of the observer, and the surroundings.
Changes in these factors can be simulated using a projector-camera setup.
Other research focuses on changing the appearance from the perspective of the projector, or on projector compensation for slightly warped planar surfaces.
This paper aims to simulate changes in the scene's appearance by actively manipulating the lighting using a projector-camera setup.
It works on not only planar surfaces, but also on more complex geometries.
This is achieved by first doing a calibration, and then using this to optimize a projection.
This projection is optimized to minimize the difference between how the scene looks when the projection is projected and the desired scene.
For low-resolution projectors, it can do this in a few seconds to half a minute.
For higher resolutions, the calibration time and file size get quite big.
This can be solved in future work using different calibration methods.

Augmented Reality (AR) tracking for mobile devices is not realiable in environments where large virtual content, such as an entire virtual building, is to be displayed to the user. This paper presents a study on how multiple on-site markers can be used to better align the virtual scene to the real-world environment to improve large-scale AR experiences. The method consists of detecting QR markers from the video stream, and then updating the position, orientation, and scale of the virtual content in order to minimize the error between the real markers and their corresponding virtual markers. This allows for more accurate tracking and can be used in more complex environments. Three different evaluation methods are proposed. Additionally, experiments were conducted to explore the influence of marker density and marker layout. These experiments show that higher density layouts as well as structured layouts lead to higher accuracy and stability.

Diminished reality (DR) is an extension of augmented reality (AR) in which real objects are concealed, removed, or replaced. State of the art DR implementations are written and evaluated on desktop platforms, and those which are aimed at smartphones use auxiliary data or hardware for image completion. This paper introduces a DR app for Android smartphones which can select, track, and remove objects in an unrestricted environment from live video at interactive speed. The application relies exclusively on RGB frames from the phone's camera as input to the DR pipeline. Multiple algorithms for object selection and tracking, as well as a patch-based inpainting approach were adapted. Selection and tracking of objects was very reliable at interactive speed. Inpainting led to convincing results for planar surfaces and unstructured textures, often delivering acceptable results even in more complicated scenes. Future optimizations are required for real-time performance of DR algorithms even on a flagship smartphone, as the hardware was a constraining factor. The performance of inpainting in particular needs to be improved to achieve acceptable frame rates.

A decrease in wellbeing worldwide due to the COVID-19 pandemic called for ways to assess wellbeing in a scalable and adequate manner. Conversational User Interfaces (CUIs) seem suitable, however, applying them optimally in certain contexts remains a challenge. This study aims to find ways to make CUIs more engaging and have a better experience by making them adaptive. A 3x2 between-subjects experiment is designed in which the effects of avatar presence, gender, and an empathic conversational style are researched. A chatbot was created in telegram, and the visual design and conversational style were altered to measure the effects on Questionnaire Experience (QX), Enjoyment, and Empathy. In total 30 participants chatted with a randomly assigned chatbot and filled in a survey about their experiences. There is no statistical preference for avatar presence or conversational style. Male gendered chatbots score higher on QX, but female chatbots are perceived as more empathic when comparing gender.

The increase in global internet users brings forth a vast amount of social media users and therefore opinions that are shared online. A subset of those users, adolescents, seem to develop some sort of addiction towards social media, which could lead to low life satisfaction. This paper tries to extract trending topics and their relation to well-being in order to help organizations like MyWellnessCheck check in on adolescents and students. The results indicated that this was possible, despite the vast amount of spam that is present online. Unsurprisingly, current events made the list of trending topics with negative sentiment like ”school shootings”, as well as unexpected topics with positive sentiment that could potentially im- prove well-being, like ”safe snacks”.

With the COVID-19 pandemic testing humanity worldwide in unforeseen ways, wellbeing assessment has stepped to the foreground of individual health status. Conversational User Interfaces (CUIs) prove promising as an assessment tool, but lacking the means to retain users engaged during the process. This research aims to explore a solution introducing a graphical modality to a CUI, that improves user experience during wellbeing assessment. A psychological projective testing technique was researched, the H-T-P test, which exposes one's personality, mood and feelings through drawing. A system was designed that utilizes a simplified version of the test and image classification to provide a new means of wellbeing assessment. The system's performance and usability show that such an approach is indeed feasible. This paper describes the design process of the system, the resulting prototype and possible improvements for future implementations.

With the surge of mental health issues during COVID-19, more emphasis has turned towards assessing wellbeing. At the same time, recent advances in AI have shown huge potential in a variety of fields. However, few solutions are available at the intersection of those two fields. This research explores how the use of transformer models like GPT-3 could have a positive impact in the domain of wellbeing and proposes a solution to automatize the survey question creation process. After comparing several GPT-3 question creation methods, it was found that through clever prompt engineering and added context, it can be possible to generate syntactically and contextually correct questions about any specific wellbeing context. In addition, the paper discusses potential ways to assess such questions and offers a demo for a question generation web application.

Due to COVID-19 the overall wellbeing worldwide decreased. Assessing and improving wellbeing became a more important subject. This article describes the design research that uses the My Wellness Check survey created by the Delft University for Technology and aims to create a dashboard for wellbeing. That includes a way of authenticating users to very sensitive data. Also, finding ways how to improve personal wellbeing by using a recommendation system based on different types of filtering and the additional elements that are needed for a recommendation system.

Quantum networks allow multiple devices to exchange information encoded within quantum systems.
Such quantum networks use classical control messages to coordinate entanglement between nodes.
Third parties which can forge such control messages may interfere with the workings of quantum links, however:
They may either perform fraudulent requests for entanglement, destroying local quantum memory of nodes as a result, or interfere with the inner workings of protocols within the quantum stack targeting the availability of the link(s).
We, therefore, conclude that all link and physical layer control messages must be transmitted via authenticated channels.
Typically one uses a Message Authentication Code (MAC) to do so, which takes as input a message and outputs a tag which is transmitted alongside the message.
Additionally, it takes as input a unique number each time (nonce) to prevent replay-type attacks.
In this work, we first investigate the use of information-theoretic MACs combined with quantum key distribution (QKD) to authenticate control messages. We find that the fastest QKD solutions provide key material at a rate that is sufficient to not become a bottleneck in current quantum links.
Second, we survey multiple computationally secure MAC solutions and benchmarks to get an indication of their performance when authenticating short messages.
While not information-theoretically secure, their latency is generally speaking greater than or equal to that of information-theoretic solutions.
Finally, we augment the existing simulation of a single quantum link by Dahlberg et al. by inserting delays based on the performance of these MACs.
The performance of the link is evaluated using the mean throughput:
The rate at which successfully entangled pairs are delivered.
We find that the introduction of transmission time overhead, without any authentication, causes a noticeable decrease in throughput of the link.
When considering an authenticated channel that uses SipHash (a popular MAC) we find that throughput decreases even further, though less significantly.
Therefore, the overall decrease in throughput appears to not be detrimental to the working of the quantum link, which remains functional even when the classical channel is authenticated.