2,268 research outputs found
Distributed Ledger Technology (DLT) Applications in Payment, Clearing, and Settlement Systems:A Study of Blockchain-Based Payment Barriers and Potential Solutions, and DLT Application in Central Bank Payment System Functions
Payment, clearing, and settlement systems are essential components of the financial markets and exert considerable influence on the overall economy. While there have been considerable technological advancements in payment systems, the conventional systems still depend on centralized architecture, with inherent limitations and risks. The emergence of Distributed ledger technology (DLT) is being regarded as a potential solution to transform payment and settlement processes and address certain challenges posed by the centralized architecture of traditional payment systems (Bank for International Settlements, 2017). While proof-of-concept projects have demonstrated the technical feasibility of DLT, significant barriers still hinder its adoption and implementation. The overarching objective of this thesis is to contribute to the developing area of DLT application in payment, clearing and settlement systems, which is still in its initial stages of applications development and lacks a substantial body of scholarly literature and empirical research. This is achieved by identifying the socio-technical barriers to adoption and diffusion of blockchain-based payment systems and the solutions proposed to address them. Furthermore, the thesis examines and classifies various applications of DLT in central bank payment system functions, offering valuable insights into the motivations, DLT platforms used, and consensus algorithms for applicable use cases. To achieve these objectives, the methodology employed involved a systematic literature review (SLR) of academic literature on blockchain-based payment systems. Furthermore, we utilized a thematic analysis approach to examine data collected from various sources regarding the use of DLT applications in central bank payment system functions, such as central bank white papers, industry reports, and policy documents. The study's findings on blockchain-based payment systems barriers and proposed solutions; challenge the prevailing emphasis on technological and regulatory barriers in the literature and industry discourse regarding the adoption and implementation of blockchain-based payment systems. It highlights the importance of considering the broader socio-technical context and identifying barriers across all five dimensions of the social technical framework, including technological, infrastructural, user practices/market, regulatory, and cultural dimensions. Furthermore, the research identified seven DLT applications in central bank payment system functions. These are grouped into three overarching themes: central banks' operational responsibilities in payment and settlement systems, issuance of central bank digital money, and regulatory oversight/supervisory functions, along with other ancillary functions. Each of these applications has unique motivations or value proposition, which is the underlying reason for utilizing in that particular use case
A Trust Management Framework for Vehicular Ad Hoc Networks
The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers
Automation for network security configuration: state of the art and research trends
The size and complexity of modern computer networks are progressively increasing, as a consequence of novel architectural paradigms such as the Internet of Things and network virtualization. Consequently, a manual orchestration and configuration of network security functions is no more feasible, in an environment where cyber attacks can dramatically exploit breaches related to any minimum configuration error. A new frontier is then the introduction of automation in network security configuration, i.e., automatically designing the architecture of security services and the configurations of network security functions, such as firewalls, VPN gateways, etc. This opportunity has been enabled by modern computer networks technologies, such as virtualization. In view of these considerations, the motivations for the introduction of automation in network security configuration are first introduced, alongside with the key automation enablers. Then, the current state of the art in this context is surveyed, focusing on both the achieved improvements and the current limitations. Finally, possible future trends in the field are illustrated
Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.
The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases
In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance
Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
Network emulation offers a flexible solution for network deployment and operations, leveraging software to consolidate all nodes in a topology and utilizing the resources of a single host system server. This research paper investigated the state of cybersecurity in virtualized systems, covering vulnerabilities, exploitation techniques, remediation methods, and deployment strategies, based on an extensive review of the related literature. We conducted a comprehensive performance evaluation and comparison of two network-emulation platforms: Graphical Network Simulator-3 (GNS3), an established open-source platform, and the SEED Internet Emulator, an emerging platform, alongside physical Cisco routers. Additionally, we present a Distributed System that seamlessly integrates network architecture and emulation capabilities. Empirical experiments assessed various performance criteria, including the bandwidth, throughput, latency, and jitter. Insights into the advantages, challenges, and limitations of each platform are provided based on the performance evaluation. Furthermore, we analyzed the deployment costs and energy consumption, focusing on the economic aspects of the proposed application
Flashpoint: A Low-latency Serverless Platform for Deep Learning Inference Serving
Recent breakthroughs in Deep Learning (DL) have led to high demand for executing inferences in interactive services such as ChatGPT and GitHub Copilot. However, these interactive services require low-latency inferences, which can only be met with GPUs and result in exorbitant operating costs. For instance, ChatGPT reportedly requires millions of U.S. dollars in cloud GPUs to serve its 1+ million users. A potential solution to meet low-latency requirements with acceptable costs is to use serverless platforms. These platforms automatically scale resources to meet user demands. However, current serverless systems have long cold starts which worsen with larger DL models and lead to poor performance during bursts of requests. Meanwhile, the demand for larger and larger DL models make it more challenging to deliver an acceptable user experience cost-effectively. While current systems over-provision GPUs to address this issue, they incur high costs in idle resources which greatly reduces the benefit of using a serverless platform.
In this thesis, we introduce Flashpoint, a GPU-based serverless platform that serves DL inferences with low latencies. Flashpoint achieves this by reducing cold start durations, especially for large DL models, making serverless computing feasible for latency-sensitive DL workloads. To reduce cold start durations, Flashpoint reduces download times by sourcing the DL model data from within the compute cluster rather than slow cloud storage. Additionally, Flashpoint minimizes in-cluster network congestion from redundant packet transfers of the same DL model to multiple machines with multicasting. Finally, Flashpoint also reduces cold start durations by automatically partitioning models and deploying them in parallel on multiple machines. The reduced cold start durations achieved by Flashpoint enable the platform to scale resource allocations elastically and complete requests with low latencies without over-provisioning expensive GPU resources.
We perform large-scale data center simulations that were parameterized with measurements our prototype implementations. We evaluate the system using six state-of-the-art DL models ranging from 499 MB to 11 GB in size. We also measure the performance of the system in representative real-world traces from Twitter and Microsoft Azure. Our results in the full-scale simulations show that Flashpoint achieves an arithmetic mean of 93.51% shorter average cold start durations, leading to 75.42% and 66.90% respective reductions in average and 99th percentile end-to-end request latencies across the DL models with the same amount of resources. These results show that Flashpoint boosts the performance of serving DL inferences on a serverless platform without increasing costs
Perceptions and Practicalities for Private Machine Learning
data they and their partners hold while maintaining data subjects' privacy. In this thesis I show that private computation, such as private machine learning, can increase end-users' acceptance of data sharing practices, but not unconditionally. There are many factors that influence end-users' privacy perceptions in this space; including the number of organizations involved and the reciprocity of any data sharing practices. End-users emphasized the importance of detailing the purpose of a computation and clarifying that inputs to private computation are not shared across organizations. End-users also struggled with the notion of protections not being guaranteed 100\%, such as in statistical based schemes, thus demonstrating a need for a thorough understanding of the risk form attacks in such applications. When training a machine learning model on private data, it is critical to understand the conditions under which that data can be protected; and when it cannot. For instance, membership inference attacks aim to violate privacy protections by determining whether specific data was used to train a particular machine learning model.
Further, the successful transition of private machine learning theoretical research to practical use must account for gaps in achieving these properties that arise due to the realities of concrete implementations, threat models, and use cases; which is not currently the case
- …