ABSTRACTCryptocurrency exchanges have become a multi‐billion dollar industry. Although these platforms are not only relevant for economic reasons but also from a privacy and legal perspective, empirical studies investigating the operations of cryptocurrency exchanges and the behavior of their users are surprisingly rare. A notable exception is a study analyzing the cryptocurrency exchange ShapeShift. While this study described new heuristics to retrieve a significant fraction of trades made on the plaform, its approach relied on identifying cryptocurrency transactions based on previously scraped trade data. This limited the analysis to the timeframe for which data had been acquired and likely led to false negatives in the transaction identification process. In this paper, we replicate and extend previous work by conducting an in‐depth investigation of the cryptocurrency exchange Evonax. Our analysis is based on actual trading data acquired by using a novel methodology allowing to extract detailed information from the public blockchain and the interface of the exchange platform. We are able to identify 30,402 transactions between the launch of Evonax in February 2018 and December 31, 2022, which should be close to a complete set of all transactions. This allows us not only to analyze the business practices of a cryptocurrency exchange but also to identify a number of interesting use cases that are likely to be associated with illegal activity. This paper is an extended version of a research article previously accepted at the CryptoEx Workshop at IEEE ICBC 2024.

ABSTRACTMultitopology routing (MTR) provides an attractive alternative to segment routing (SR) for traffic engineering when network devices cannot be upgraded. However, due to a high overhead in terms of link state messages exchanged by topologies and the need to frequently update link weights to follow evolving network conditions, MTR is often limited to a small number of topologies and the satisfaction of loose QoS constraints. To overcome these limitations, we propose virtual MTR (vMTR), an MTR extension where demands are routed over virtual topologies that are silent; that is, they do not exchange LSA messages and that are continuously derived from a very limited set of real topologies, optimizing each QoS parameter. In this context, we present a polynomial and exact algorithm for vMTR and, as a benchmark, a local search algorithm for MTR. We show that vMTR helps to reduce drastically the number of real topologies and that it is more robust to QoS changes. In the case where SR can actually be rolled‐out, we also show that vMTR allows to drastically reduce SR overhead.

ABSTRACTIn this study, we propose and evaluate a model that utilizes both log data and state data to detect abnormal conditions in network switches. Building upon our previous research and drawing inspiration from TF‐IDF used in natural language processing to measure word importance, we propose a statistical method, Log‐TF‐IDF, to quantify the rarity of each log pattern in the log data. Furthermore, based on this Log‐TF‐IDF, we introduce the AB Score, which quantifies how abnormal the current log pattern is. Our findings indicate that the AB Score is notably higher and more volatile in abnormal conditions. We confirm that anomaly detection is feasible through the AB Score, which has the advantage of being computationally efficient due to its statistical basis. We combined the metrics generated during the AB Score calculation with resource data collected with NETCONF and developed a machine‐learning model to detect abnormal conditions in network switches. We confirm that this model can detect abnormal conditions with an F1 score of 0.86 on our collected dataset, confirming its viability for detecting abnormal states in network equipment.

ABSTRACTThe information society has led to a shift in traditional English education methods, with the evolution of technology, particularly internet and communication network technologies, reshaping the teaching landscape. This facilitated innovative instructional approaches and enhanced the learning experience. This research introduces a novel virtual learn net architecture (VLNA) within the 6G network layers, which processes the performance of the virtual reality‐based English education system (VR‐EES) model to provide a seamless, personalized learning experience for online learners. This architecture is structured into several layers: The user equipment (UE) layer connects VR headsets to the network with ultrareliable, low‐latency links; the radio access network (RAN) layer, employing massive MIMO and beam forming, enhances connection speed, capacity, and coverage. Edge computing handles latency‐sensitive tasks like speech recognition and adaptive content delivery, reducing the load on the core network. The core network layer (CLN) manages network slices for specific learning tasks such as real‐time interaction, high‐definition multimedia, and computation‐intensive processes, with control plane and user plane separation (CUPS) optimizing network management and security through end‐to‐end encryption. Software‐defined networking (SDN) and network function virtualization (NFV) provide centralized, dynamic control, allowing real‐time resource allocation based on demand. Cloud‐edge integration supports Artificial intelligence (AI)‐driven adaptive learning, optimizing educational content delivery based on individual progress. The study results demonstrate that stimulation of VLNA achieved significant improvements in latency reduction, bandwidth utilization, throughput, packet loss rate, jitter, user engagement, learning efficiency, and user satisfaction. The integration of edge computing and network slicing led to a significant reduction in latency, while the enhanced throughput enabled seamless VR experiences. In this study, latency reduction, bandwidth utilization, and user satisfaction emerge as the most significant factors, with user satisfaction standing out as the top performer due to its substantial impact on enhancing the overall learning experience. The packet loss rate is maintained to a certain level, ensuring reliable data transmission. The VR‐EES model's experimental results also enhanced visual learning, multimedia quality, user pleasure, learning effectiveness, and user engagement.

ABSTRACTThe rapid advancement of communication technologies, particularly in English language learning, is sharing education with the implementation of sixth‐generation (6G) networks, offering immersive and interactive learning experiences. The purpose of the research is to establish an advanced method for sharing English education resources across multiple virtual networks enabled by 6G technology. Traditional resource‐sharing systems lack the effectiveness and optimization requirement for large‐scale instructional assignments, especially in virtual settings with various user demands. To address this, the study proposed a novel Dynamic Tunicate Swarm Refined Graph Neural Networks (DTS‐RGNN) model to optimize resource allocation and improve the efficiency of resource sharing among educational tasks. The approach uses TSO for resource allocation scalable through 6G technology and GNN for task assignment according to the previous performances and interaction with the students to balance resource utilization. The experimental group performed writing (90%), sharing (91%), listening (85%), and reading (75%), finishing the task in 5.5 s at 1000 GB. Throughput increased by 5.0 GBps and resource utilization efficiency improved to (96%) and student outcomes showed high satisfaction (93%), retention (89%), and engagement (90%). The findings demonstrated the proposed method significantly improves the sharing of online English education resources, promoting more interactive and effective language learning experiences in virtual networks.

ABSTRACTThe rapid advancement of communication technologies, particularly in English language learning, is sharing education with the implementation of sixth‐generation (6G) networks, offering immersive and interactive learning experiences. The purpose of the research is to establish an advanced method for sharing English education resources across multiple virtual networks enabled by 6G technology. Traditional resource‐sharing systems lack the effectiveness and optimization requirement for large‐scale instructional assignments, especially in virtual settings with various user demands. To address this, the study proposed a novel Dynamic Tunicate Swarm Refined Graph Neural Networks (DTS‐RGNN) model to optimize resource allocation and improve the efficiency of resource sharing among educational tasks. The approach uses TSO for resource allocation scalable through 6G technology and GNN for task assignment according to the previous performances and interaction with the students to balance resource utilization. The experimental group performed writing (90%), sharing (91%), listening (85%), and reading (75%), finishing the task in 5.5 s at 1000 GB. Throughput increased by 5.0 GBps and resource utilization efficiency improved to (96%) and student outcomes showed high satisfaction (93%), retention (89%), and engagement (90%). The findings demonstrated the proposed method significantly improves the sharing of online English education resources, promoting more interactive and effective language learning experiences in virtual networks.