The training of diffusion-based models for image generation is predominantly controlled by a select few Big Tech companies, raising concerns about privacy, copyright, and data authority due to the lack of transparency regarding training data. Hence, we propose a federated diffusion model scheme that enables the independent and collaborative training of diffusion models without exposing local data. Our approach adapts the Federated Averaging (FedAvg) algorithm to train a Denoising Diffusion Probabilistic Model (DDPM). Through a novel utilization of the underlying UNet backbone, we achieve a significant reduction of up to 74% in the number of parameters exchanged during training, compared to the naive FedAvg approach, whilst simultaneously maintaining image quality comparable to the centralized setting, as evaluated by the FID score.

Distributed Denial of Service (DDoS) leverages the power of multiple servers to disrupt the operations of a victim service. Due to the financial risks posed by downtimes on critical online infrastructure, DDoS is among the top threats in the cybersecurity landscape. In this paper, we analyze the characteristics of previously launched DDoS attacks using collected network data. To extract the characteristics from a network trace file, we expand the DDoS Dissector tool with additional statistics representing the peak traffic strength and the sources of the attack. In addition, we implement an algorithm to parallelize the analysis of large-scale attacks when executed in memory-constrained environments. Our results show that the error difference in the statistics obtained when running the parallelized version and the original one is less than 0.5%. Furthermore, we investigate several DDoS attacks by analyzing the contained attack vectors and their corresponding characteristics. Our software correctly detects the attack vectors, however, we remark that the output quality is impacted by the percentage of non-attack traffic. In particular, we provide an overview of the current state of the DDoS landscape seen from the point of view of a scrubbing service and study the effect of a Booter takedown on the frequency of DDoS attacks. Lastly, we introduce spoof detection techniques based on the time-to-live value found in the packet headers. From the spoofing analysis, we can deduce the distribution of operating systems that make up the sources of the attack.

Touchless interaction with computers has become more important in recent years, especially in the context of the COVID-19 pandemic. Applications include situations where touch input is not possible or not desirable, e.g. for hygienic purposes in a public setting or a medical setting. Practical examples for touchless interaction include elevators, vending machines, and other public devices that are used by many people. However, most touchless interaction systems are expensive and require significant computational power. This paper proposes a bare-bones low-power and low-cost system for recognizing air-written digits using a microcontroller and light sensitivity sensors. A proof of concept has been created and tested in a fixed lighting scenario with a fixed set of gestures for the digits 0 to 9 to show the feasibility of such a system. The system uses a convolutional neural network to recognize digits and achieves an average accuracy of 58,8% on a validation set of unseen participants. It performs significantly better on new samples from users already seen during training, achieving an average accuracy of 93,5%.

This paper presents how a convolutional neural network can be constructed in order to recognise gestures using photodiodes and ambient light. A number of candidates are presented and evaluated, with the most performant being adopted for in-depth analysis. This network is then compressed in order to be ran on an Arduino Nano 33 BLE microcontroller to present its feasibility in embedded operation. The final utilised network was observed to have accuracies between 75.4% and 86.8% depending on the testing conditions. Further, all candidates were found to be sufficiently compact and low-latency for real-time operation.

Federated Continual Learning (FCL) is a emerging field with strong roots in Image classification. However, limited research has been done on its potential in Natural Language Processing and Tabular datasets. With recent developments in A.I. with language models and the widespread use of mobile devices, it becomes relevant to consider FCL’s capabilities in dynamic environments. Our paper discusses and evaluates the applicability of FCL methods between the domains of Natural Language Processing Tabular Data with Image Processing as a baseline. We use Long-Short Term Memory (LSTM) models, DNN’s and LeNet-5 as models for Sentiment analysis, Tabular classification and Image classification. Through our experiments, we evaluate the average accuracy and backwards transfer of EWC, GEM, their federated variants and the state-of-the-art FCL method FedWEIT. With these methods, sentiment analysis and tabular classification tasks show that image classification reached over 17% higher average accuracy and achieved a 99.5% average increase in knowledge transfer between tasks. Furthermore, we observe that non-federated continual learning methods on average reach higher accuracies than their federated counterparts as well as the state-of-the-art methods

The thesis focuses on investigating the role of IP Multimedia Subsystem (IMS) in 5G networks. IMS already plays a very important role in enabling a wide range of real-time multimedia communication services such as basic phone calls and messaging in the LTE network. Addition of application servers on top of the IMS core can provide enhanced functionalities like presence, advanced messaging and SIP trunking. IMS guarantees quality, security and reliability of multimedia services when serving users without the installation of any application as well as flexibility over access. This sets IMS apart from other third party applications found on the internet. IMS was created with the idea of being adaptable to the evolving technology. With the implementation of the 5G network underway, a study of the impact of this new architecture on the IMS services is imminent. The thesis focuses on the study of different network elements participating in an IMS service as well as investigating IMS voice and video calls over the 5G network. The thesis consists of two parts: The first part involves exploring the role of IMS in 5G networks. A brief overview of the evolution of mobile networks will help understand the differences between 1G/2G/3G/4G. Following this, IMS and its role in enabling multimedia services in the LTE network is explored. Next, the 5G System Architecture is explained along with components and their functions. Next, a comparison between the elements in 4G and 5G provides a clear understanding of the technological evolution and the procedure involved. The next steps would include understanding the IMS call flow in LTE networks. This would provide a good foundation to understand how the IMS services will be provided over the 5G network. The second part of the thesis includes testing the voice and video services over Ericsson’s 5G network at their Rijen office. As a starting step, the voice and video services are tested using WebRTC. Upon succeeding in the peer-to-peer test, the next step is establishing connectivity to Ericsson’s IMS network at Kista, Sweden. This will allow the testing of IMS voice and video services over the 5G network. The results are recorded and analysed. These are in agreement with the theoretical expectations.