Towards automated discovery of access control vulnerabilities

This thesis is a research into developing a methodology and implementation of automated gray-box Broken Access Control Scanning (BACS) in web applications. Broken access controls take first place in the OWASP Top Ten Web Application Security Risks 2021. The need for this research comes from the observation that testing for broken access controls...

Tikhonov and Sobolev regularisers compared to user-based KNN collaborative filtering

Collaborative filtering is used to predict the preference or rating of a user for a certain item. Collaborative filtering is based on the notion that similar users rate similarly. A lot of research is done on how to improve this algorithm, mostly with deep learning. A less investigated field for recommender systems is graph signal processing....

Total Variation Regularisation for Item KNN Collaborative Filtering: Performance Analysis

Algorithms that recommend items to users are known as recommender systems and have become an important part of online ecosystems. These systems calculate the similarity between given users or items based on the ratings they have received. Such similarities can be modeled as a graph where users or items (nodes) have connections (edges) to other...

The Performance of Total Variation Regularizer for User Collaborative Filtering

Recommender systems (RS) assist users in making decisions by filtering content that the user would likely find relevant. Standard techniques like collaborative filtering exploit user similarities to find the recommendations assuming that similar users are likely to be interested in the same items. On the other hand, graph RS borrow techniques...

Item-Item Collaborative Filtering via Graph Regularization

A recommendation algorithm aims to predict the quality of a user's future interaction with certain items based on their previous interactions. As research progresses, these algorithms are becoming increasingly more complicated with the use of machine learning and neural networks. This paper looks into a more simple solution. The recommendation...

Time-Sensitive Networking IEEE 802.1CB: Security and Reliability

The upcoming IEEE 802.1CB standard aims to solve performance and reliability issues in Time-Sensitive Networking (TSN). Mission-critical systems often use these standards for communication in automotive, industrial, and avionic networks. However, researchers did not sufficiently investigate the security risks and possible mitigation solutions to...

Secure Proximity Detection and Verification: Addressing vulnerabilities in IEEE 802.15.4z UWB

We live in a world where much of our interactions with the environment around us depend on us being physically close to them. For instance, we have proximity­based tokens (e.g., keys and smartcards) for access systems installed at various places such as in cars, at contactless payment terminals, and in electronic passports. Moreover, such...

Adding Bloom to High-Dynamic-Range Tone Mapping

We present a technique for enhancing high-dynamic-range tone mapping algorithms by adding the bloom effect to bright areas. Bloom is based on the fact that real-life lenses convolve light and make bright areas emit a glow. The algorithm takes a set of images with different exposures as input, and performs a tone mapping algorithm on these. It...

Image-to-Image Translation of Synthetic Samples for Rare Classes

The natural world is long-tailed: rare classes are observed orders of magnitudes less frequently than common ones, leading to highly-imbalanced data where rare classes can have only handfuls of examples. Learning from few examples is a known challenge for deep learning based classification algorithms, and is the focus of the field of low-shot...

Transferring a Segmentation Task Between Real and Synthetic Data

In the field of ecology, camera traps are important tools to collect information on the wildlife of certain areas. The problem that arises with many camera traps is that they can collect more images than a human can realistically go trough all by themselves. To help classify these images computer vision is proposed as an alternative to manual...

Hidden in plain sight: Camera tracking markers forvirtual production setups

With a large move to virtual production in films, where a large screen displaying a rendered scene is used as an alternative to replacing a green screen, there is a need for simple and effective camera tracking. Infrared tracking is widely used in the industry however it creates a barrier to entry for small productions without limited budgets....

Matting using a known background and a chroma-key minimization scheme

In this paper, we investigate the problem of segmenting the foreground objects from the known background. The target application is a setting where a large display serves as the backdrop for a scene. The method proposed in this paper solves this problem in two steps. We try to separate the foreground and the background with the modified...

Domain Adaptation for Rare Classes Augmented with Synthetic Samples

To alleviate lower classification performance on rare classes in imbalanced datasets, a possible solution is to augment the underrepresented classes with synthetic samples. Domain adaptation can be incorporated in a classifier to decrease the domain discrepancy between real and synthetic samples. While domain adaptation is...

Capturing and grouping SDR frames containing sections of HDR from a video feed to artificially expand the dynamic range of SDR screens

In this paper, a method is proposed to artificially expand the dynamic range of screens with a limited dynamic range. This research is linked to a new film-making technology where, instead of using a green screen, the background of a scene is displayed on a screen in real time using a computer generated background. This provides real time...

Data-Efficient GAN for Synthetic Samples of Rare Classes

Camera traps are used around the world to provide data on species, population sizes and how species are interacting. However this creates a lot of work in identifying which animal was actually spotted near the camera. Attempts have been made to use deep-learning to identify animals and work correctly for animals which are not rare but the lack...