1,778 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN

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    Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community\u27s attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques\u27 design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research

    Undergraduate Catalog of Studies, 2022-2023

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    Deteção de intrusões de rede baseada em anomalias

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    Dissertação de mestrado integrado em Eletrónica Industrial e ComputadoresAo longo dos últimos anos, a segurança de hardware e software tornou-se uma grande preocupação. À medida que a complexidade dos sistemas aumenta, as suas vulnerabilidades a sofisticadas técnicas de ataque têm proporcionalmente escalado. Frequentemente o problema reside na heterogenidade de dispositivos conectados ao veículo, tornando difícil a convergência da monitorização de todos os protocolos num único produto de segurança. Por esse motivo, o mercado requer ferramentas mais avançadas para a monitorizar ambientes críticos à vida humana, tais como os nossos automóveis. Considerando que existem várias formas de interagir com os sistemas de entretenimento do automóvel como o Bluetooth, o Wi-fi ou CDs multimédia, a necessidade de auditar as suas interfaces tornou-se uma prioridade, uma vez que elas representam um sério meio de aceeso à rede interna do carro. Atualmente, os mecanismos de segurança de um carro focam-se na monitotização da rede CAN, deixando para trás as tecnologias referidas e não contemplando os sistemas não críticos. Como exemplo disso, o Bluetooth traz desafios diferentes da rede CAN, uma vez que interage diretamente com o utilizador e está exposto a ataques externos. Uma abordagem alternativa para tornar o automóvel num sistema mais robusto é manter sob supervisão as comunicações que com este são estabelecidas. Ao implementar uma detecção de intrusão baseada em anomalias, esta dissertação visa analisar o protocolo Bluetooth no sentido de identificar interações anormais que possam alertar para uma situação fora dos padrões de utilização. Em última análise, este produto de software embebido incorpora uma grande margem de auto-aprendizagem, que é vital para enfrentar quaisquer ameaças desconhecidas e aumentar os níveis de segurança globais. Ao longo deste documento, apresentamos o estudo do problema seguido de uma metodologia alternativa que implementa um algoritmo baseado numa LSTM para prever a sequência de comandos HCI correspondentes a tráfego Bluetooth normal. Os resultados mostram a forma como esta abordagem pode impactar a deteção de intrusões nestes ambientes ao demonstrar uma grande capacidade para identificar padrões anómalos no conjunto de dados considerado.In the last few years, hardware and software security have become a major concern. As the systems’ complexity increases, its vulnerabilities to several sophisticated attack techniques have escalated likewise. Quite often, the problem lies in the heterogeneity of the devices connected to the vehicle, making it difficult to converge the monitoring systems of all existing protocols into one security product. Thereby, the market requires more refined tools to monitor life-risky environments such as personal vehicles. Considering that there are several ways to interact with the car’s infotainment system, such as Wi-fi, Bluetooth, or CD player, the need to audit these interfaces has become a priority as they represent a serious channel to reach the internal car network. Nowadays, security in car networks focuses on CAN bus monitoring, leaving behind the aforementioned technologies and not contemplating other non-critical systems. As an example of these concerns, Bluetooth brings different challenges compared to CAN as it interacts directly with the user, being exposed to external attacks. An alternative approach to converting modern vehicles and their set of computers into more robust systems is to keep track of established communications with them. By enforcing anomaly-based intrusion detection this dissertation aims to analyze the Bluetooth protocol to identify abnormal user interactions that may alert for a non conforming pattern. Ultimately, such embedded software product incorporates a self-learning edge, which is vital to face newly developed threats and increasing global security levels. Throughout this document, we present the study case followed by an alternative methodology that implements an LSTM based algorithm to predict a sequence of HCI commands corresponding to normal Bluetooth traffic. The results show how this approach can impact intrusion detection in such environments by expressing a high capability of identifying abnormal patterns in the considered data

    Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

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    Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation

    Comparing the Performance of Initial Coin Offerings to Crowdfunded Equity Ventures

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    Uncertainty in markets increases the likelihood of market failure due to volatility and suboptimal functioning. While initial coin offerings (ICOs) and crowdfunded equity (CFE) offerings may improve functioning in growing markets, there is a lack of knowledge and understanding pertaining to the relative efficiency and behavior of ICO markets compared to CFE markets, potentially perpetuating and thwarting the various communities they are intended to serve. The purpose of this correlational study was to compare a group of ICOs with a group of CFE offerings to identify predictive factors of funding outcomes related to both capital offering types. Efficient market hypothesis was the study’s theoretical foundation, and analysis of variance was used to answer the research question, which examined whether capital offering type predicted the amount of funds raised while controlling for access to the offering companies’ secondary control factors: historical financial data, pro forma financial projections, detailed product descriptions, video of product demonstrations, company website, company history, company leadership, and company investors. Relying on a random sample of 115 campaigns (84 ICOs and 31 CFE) from websites ICOdrops.com, localstake.com, fundable.com, and mainvest.com, results showed differences in mean funds raised between CFEs and ICOs (346,075comparedto346,075 compared to 4,756,464, respectively). ANOVA results showed no single secondary control factors and only one two-factor interaction (company leadership and company investors) influenced mean funds raised. This study may contribute to positive social change by informing best practices among market participants including entrepreneurs, regulators, scholars, and investors

    Resource Allocation in Networking and Computing Systems: A Security and Dependability Perspective

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    In recent years, there has been a trend to integrate networking and computing systems, whose management is getting increasingly complex. Resource allocation is one of the crucial aspects of managing such systems and is affected by this increased complexity. Resource allocation strategies aim to effectively maximize performance, system utilization, and profit by considering virtualization technologies, heterogeneous resources, context awareness, and other features. In such complex scenario, security and dependability are vital concerns that need to be considered in future computing and networking systems in order to provide the future advanced services, such as mission-critical applications. This paper provides a comprehensive survey of existing literature that considers security and dependability for resource allocation in computing and networking systems. The current research works are categorized by considering the allocated type of resources for different technologies, scenarios, issues, attributes, and solutions. The paper presents the research works on resource allocation that includes security and dependability, both singularly and jointly. The future research directions on resource allocation are also discussed. The paper shows how there are only a few works that, even singularly, consider security and dependability in resource allocation in the future computing and networking systems and highlights the importance of jointly considering security and dependability and the need for intelligent, adaptive and robust solutions. This paper aims to help the researchers effectively consider security and dependability in future networking and computing systems.publishedVersio
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